Methods and systems for testing of eyeglasses

ABSTRACT

A methods and system for testing of eyeglasses using a background object are disclosed. The method includes obtaining an image of a background object, whereby in at least a part of the image the background object is captured as viewed through a lens of a pair of eyeglasses, and analyzing at least a part of the image showing the background object being captured through a lens of the eyeglasses, and identifying a property of the lens based on the analyzing.

TECHNOLOGICAL FIELD

The present invention relates to methods and systems for testing ofeyeglasses.

BACKGROUND

In recent years, visual health and protection from harmful lightaccounts for a growing share of the research and innovation efforts bymanufacturers of lenses for use in eyewear.

Indeed, today, lenses may be manufactured with a wide variety offeatures such as filters or protective layers for protection against UV(Ultraviolet) light, HEV (High-Energy Visible) light, etc.

However, the tools currently available for detecting the presence offeatures such as filters or layers on lenses are limited to professionalequipment which is usually adapted for use in an industrial setting(e.g. designed for being operated by a QA Engineer or an opticaltechnician), and are not readily available for most eyeglass wearers.

Further, the professional equipment often requires complex operation bya professional worker, particularly in as far as analyzing measurementstaken using such tools is concerned.

Consequently, with the growing variety of filters and layers that may beapplied to lenses in use on eyewear like sunglasses or other eyeglasses,a person who purchases eyeglasses has to rely on information receivedfrom a seller or manufacturer (if available).

Customers have no way to independently detect or verify 5 the presenceof features like specific filters or protective layers on the lensesinstalled on the eyeglasses they purchased.

GENERAL DESCRIPTION

The present invention provides methods and systems (apparatus) fortesting of eyeglasses using a background object.

Lenses in modern eyeglasses have a variety of properties which may beselected to best fit the different needs of individual user ofeyeglasses, say different filters or protective layers—for protectionagainst UV (Ultraviolet) light or HEV (High-Energy Visible) light, orother features, as known in the art.

However, the tools currently available for detecting the presence offeatures such as filters or layers on lenses are limited to professionaltools for use (say by 10 optical technicians) in an industrialenvironment, and are not available for most users of eyeglasses.

Consequently, with a growing variety of filters and layers that may beapplied to lenses in use on eyewear such as sunglasses or othereyeglasses, a person who purchases eyeglasses has to rely on informationreceived from a seller or manufacturer, and has no way to verity theinformation.

According various embodiments of the present invention, there isprovided a method of testing of eyeglasses using a background object,which may also serve an individual user. The method provides fordetermining/estimating at least one property of the lens(s) of theeyeglasses, such as coating/filter type found on in the lens, and/ordefects such as scratches cracks and/or peelings associated blurrinessof the lens. The method can be performed in-situ, by the end user, andby conventional general purpose equipment such as smart mobile devicesthat are typically available to the end user and used for various otherpurposes.

Thus according to a first broad aspect of the present invention there isprovided a method for determining parameters of eyeglasses lens. Themethod include: obtaining an image of a background object, whereby in atleast a part of the image at least a part of the background object iscaptured as viewed through a lens of a pair of eyeglasses; and analyzingsaid at least a part of the image to identifying a property of the lens.

The analyzing comprises comparing two parts of the background object ascaptured in the image, only one of the two parts being a part capturedthrough the lens.

The analyzing may be adapted for analyzing a color characteristic of theimage. The property of the lens which is to be determined may be atleast one of the group consisting of having a predefined coating andhaving a predefined filter.

In some embodiments of the present invention the lens property, which isbeing estimated/determined pertains a driving compatibility of the lens.In some embodiments the lens property which is beingestimated/determined pertains to a color of the lens. In someembodiments the lens property which is being estimated/determinedpertains to the opacity of the lens.

According to various embodiments of the present invention the methodalso includes identifying an assembly quality of the eyeglasses based onthe analysis.

According to various embodiments of the present invention the method(the analyzing) includes comparing sharpness between two parts of thebackground object as captured in the image, only one of the two partsbeing a part captured through the lens.

According to various embodiments of the present invention the method(the analyzing) includes comparing a color characteristic between twoparts of the background object as captured in the image, only one of thetwo parts being a part captured through the lens.

According to various embodiments of the present invention the method(the analyzing) includes identifying a predefined deformation along asegment within the background object as captured in the image.

According to various embodiments of the present invention the methodincludes optimizing color selection for at least a part of thebackground object according to technical characteristics of an imagecapture device (e.g. the white balance thereof) intended to be used forcapturing the image, a device intended to be used for presenting thebackground object, or of both of the devices.

In some embodiments a predetermined/defined reference object (i.e.object) is used in the method.

In some cases the reference/background object includes a plurality ofparts, each part having a respective, predefined color and a respective,predefined position within the background object. To this end theanalyzing of the image of the reference object may be based on therespective predefined color and position of at least one of the parts.In some embodiments, the background object include a plurality of partsarranged around a center of the background object, each part having arespective, predefined color and a respective, predefined order ofplacement around the center. The analyzing of such object may be basedon the respective predefined order of placement and color of at leastone of the parts.

In some embodiments the method further includes using a predefinedmarking appearing on the background object, for automaticallyidentifying an orientation of the background object as captured in theimage. For instance the method may include using a directional aspect ofa texture of the background object as captured in the image, forautomatically identifying an orientation of the background object ascaptured in the image. Alternatively or additionally the method mayfurther include identifying alignment of the background object ascaptured in the image in a predefined orientation, and automaticallyinitiating the analyzing upon the identifying of the alignment in thepredefined orientation. Yet alternatively or additionally, the methodmay further include identifying alignment of the background object ascaptured in the image, and using the identified alignment for guiding auser in aligning the pair of eyeglasses and an image capture device usedto capture the image with respect to each other. Yet in some embodimentsthe method includes locating a facial feature in the capture image, andusing the located facial feature for guiding a user in aligning the pairof eyeglasses and an image capture device used to capture the image withrespect to each other.

According to some embodiments the method of the present inventionincludes locating a boundary (edge) of the lens in the image. In somecases the located boundary/edge is used for guiding a user in aligningthe pair of eyeglasses and an image capture device used to capture theimage with respect to each other. In some embodiments (e.g. particularlywhen the reference object is a virtual object presented on a screen),the method includes utilizing the located boundary for verifying theappearance of the background object on the screen such that backgroundobject as captured in the image extends over two sides of the boundary

According to some embodiments, the method further includes automaticallyestimating a location of a center of the lens of the eyeglasses in theimage. Then the estimate center location may be used for guiding a userin aligning the pair of eyeglasses and an image capture device used tocapture the image with respect to each other.

According to yet another broad aspect of the present invention there isprovided a system/apparatus for testing of eyeglasses using a backgroundobject. The system includes:

-   -   a reference object image provider module configured and operable        for obtaining an image of a background object, whereby in at        least a pat of the image at least a part of the background        object is captured as viewed through a lens of a pair of        eyeglasses;    -   an image analyzer configured to analyze an image of a background        object to determine at least said part of the image in which at        least said part of the background object being captured in the        image as viewed through the lens of the eyeglasses; and    -   a property identifier, in communication with the image analyzer,        configured to identify a property of the lens based on the        analyzed image.

According to yet another broad aspect of the present invention there isprovided a non-transitory computer readable medium storing computerexecutable instructions for performing steps of testing of eyeglassesusing a background object, the steps include:

-   -   obtaining an image of a background object, whereby in at least a        part of the image at least a part of the background object is        captured as viewed through a lens of a pair of eyeglasses; and    -   analyzing said at least a part of the image and identifying a        property of the lens based on the analyzing.

Optionally according to some embodiments of the present invention themethod is implemented on a mobile device such as smart phone (e.g. anApple iPhone® or a Samsung Galaxy® Smart Phone), Tablet Computer,etc.—i.e. using devices available to any user nowadays, as described infurther detail hereinbelow.

Consequently, a user may be able to test his eyeglasses andmeasure/estimate properties of their lenses without professional toolsof the sort used in industrial environments or the help of a technicianor engineer, say at the user's home or office.

The method may help the user figure out, for example, if the lenses inthe user's eyeglasses have a HEV (High-Energy Visible) light protectivelayer, an AR (Anti-reflection) coating, etc., or another property, asdescribed in further detail hereinbelow.

According to an exemplary embodiment of the present invention, there isanalyzed an image which captures a background object, such that at leasta part of the background object is captured in the image through a lensof a pair of eyeglasses.

Then, based on the analysis of the image, there is identified one ormore properties of the lens, as described in further detail hereinbelow.

In one example, the background object is a predefined pattern presentedon a screen (say a computer screen), say a radial pattern made ofseveral slices arranged around the person's center, as described infurther detail hereinbelow.

In the example, the pair of eyeglasses may be placed on a surface, sayon a desktop, in a position opposite the computer screen on which thepredefined pattern is presented, such that at least a part of thebackground object may be viewed through one of the eyeglasses' lenses.

Next, an image of the background object in which at least a part of theobject is viewed through the lens (and typically another part is viewednot through the lens) is captured, by a system of the present inventionwhich may be incorporated/integrated with a device (e.g. a smart mobiledevice) of the user. The system may be implemented for example in anapplication (say an iPhone® App) downloadable to a user's mobile smartphone. The system may be adapted to utilize/operate the imager/cameralmodule of the mobile device of the user for capturing the image of thebackground object. The image may be captured when the user pushes acertain button on his smart mobile device/phone, or rather automaticallyupon alignment of the phone's camera and lens in a predefinedorientation with respect to each other, as described in further detailhereinbelow.

In the image of the example, the lens covers only a part of thebackground object. Consequently, only a part of the background object iscaptured through the lens, and the remaining part of the backgroundobject is captured directly—i.e. not through the lens, as described infurther detail hereinbelow, and as illustrated, for example, in FIG. 9.

Subsequently, the image is analyzed, say through one or more comparisonsmade between the background object's part that is captured in the imagethrough the lens and the background object's part that is captured inthe image, but not through the lens.

Finally, based on the analysis of the image, there may be identified aproperty of the lens. For instance spectral filtering properties of thelens can be determined (e.g. the presence of a feature such as a filteror a layer for protection against UV (Ultraviolet) light or HEV(High-Energy Visible) light), driving compatibility of the lensdistortions/defects related to the presence of defects as scratches,peeling, cracks, etc.—on the lens.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is herein described, by way of example only, withreference to the accompanying drawings. With specific reference made tothe drawings in detail, it is stressed that the particulars shown are byway of example and for purposes of illustrative discussion of certainembodiments of the present invention only, and are presented in order toprovide what is believed to be a useful and readily understooddescription of the principles and conceptual aspects of the invention.The description taken with the drawings making apparent to those skilledin the art how the several forms of the invention may be embodied inpractice.

In the drawings:

FIG. 1A is a block diagram schematically illustrating a first exemplaryapparatus for testing of eyeglasses using a background object, accordingto an exemplary embodiment of the present invention.

FIG. 1B is a block diagram schematically illustrating a second exemplaryapparatus for testing of eyeglasses using a background object, accordingto an exemplary embodiment of the present invention.

FIG. 2A is a flowchart illustrating a first exemplary computerimplemented method for testing of eyeglasses using a background object,according to an exemplary embodiment of the present invention.

FIG. 2B is a flowchart illustrating a second exemplary computerimplemented method for testing of eyeglasses using a background object,according to an exemplary embodiment of the present invention.

FIG. 3 is a simplified diagram schematically illustrating an exemplarygraph depicting an emission spectrum of one exemplary digital screen.

FIG. 4 is a simplified diagram schematically illustrating an exemplaryset of three graphs, depicting sensitivity of one exemplary digitalcamera to three primary colors.

FIG. 5 is a simplified diagram schematically illustrating a firstexemplary radial predefined pattern, according to an exemplaryembodiment of the present invention.

FIG. 6 is a simplified diagram schematically illustrating a firstexemplary planar predefined pattern, according to an exemplaryembodiment of the present invention.

FIG. 7 is a simplified diagram schematically illustrating a secondexemplary planar predefined pattern, according to an exemplaryembodiment of the present invention.

FIG. 8 is a simplified diagram schematically illustrating a secondexemplary radial predefined pattern, according to an exemplaryembodiment of the present invention.

FIG. 9 is a simplified diagram schematically illustrating an exemplaryscenario of testing of eyeglasses using a background object, accordingto an exemplary embodiment of the present invention.

FIG. 10A is a block diagram schematically illustrating a first exemplarycomputer readable medium storing computer executable instructions forpreforming steps of testing of eyeglasses using a background object,according to an exemplary embodiment of the present invention.

FIG. 10B is a block diagram schematically illustrating a secondexemplary computer readable medium storing computer executableinstructions for performing steps of testing of eyeglasses using abackground object, according to an exemplary embodiment of the presentinvention.

DETAILED DESCRIPTION

The principles and operation of the system (apparatus), method, and/or acomputer readable software product, implementing the present invention,may be better understood with reference to the drawings and accompanyingdescription.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not limited in its applicationto the details of construction and the arrangement of the components setforth in the following description or illustrated in the drawings.

The invention is capable of other embodiments or of being practiced orcarried out in various ways. Also, it is to be understood that thephraseology and terminology employed herein is for the purpose ofdescription and should not be regarded as limiting.

Reference is now made to FIG. 1A, which is a block diagram schematicallyillustrating a first exemplary system 1000 for testing of eyeglassesaccording to some embodiments of the present invention by utilizing abackground object.

System 1000 includes a Reference Object Image Provider module 110 (alsoreferred to herein as image capturer) configured and operable forproviding/capturing one or more stills or video images of a backgroundobject, an image analyzer module 120 being in communication with theimage capturer 110, and configured and operable for analyzing the imageprovided by the image capturer 110, and Lens Properties Analyzer 130(also referred to herein as property identifier) in communication withthe image analyzer 120, and configured and operable fordetermining/estimating one or more properties of the lens installed onthe eyeglasses based on the analysis of the image by the image analyzer120.

The image capturer (namely the reference object image provider module)110 is connectable to an imager 192 (e.g. a camera or other imagecapturing device), and is adapted to acquire from the imager 192 animage of the reference object 196.

In some embodiments the image capturer 110 includes a manual and/or autoimage capture modules, 112 and/or 113 respectively, which are configuredand operable for receiving operational instructions (input/signals) fromthe user of the system for capturing an image of the reference object196. The image capturer 110 also includes a camera trigger module 115(e.g. camera driver/operation utility) which is connectable to thecamera/imager 192 and configured and operable for issuing operationalinstructions/data for activating the camera to capture images. To thisend, upon receiving instructions from the user, the manual—and/orauto—image capture modules, 112 and/or 113, operate the camera triggermodule 115 to trigger the camera 196 into operation. The image capturer110 further includes an image receiver module 118 connectable to thecamera 192 and adapted to receive images captured thereby. Accordingly,once the camera 192 is triggered the image receiver module 118 obtainsthe image captured by the camera.

In some implementations for camera trigger module 115 includes a CameraParameter Adjuster 116 (e.g. auto white-balance tuner, flashdisabler/enabler or other) which is configured and operable foradjusting the camera parameters before the image is captured. Theoperation of the a Camera Parameter Adjuster 116 and its use for thesubject matter of the present invention will be discussed in modedetails below, in relation to the configurations and operations of thespectral image analyzer 124 and/or scratch/defects analyzer 126sub-modules of the image analyzer 120.

In some embodiments, the image capturer 110 is adapted to naively obtainthe image that is captured by the camera. To this end, the manual imagecapture module 113, operates upon the user input to trigger the cameraoperation.

However, in some embodiments the image capturer 110 is configured andoperable for providing only images that fulfil certain prerequisitessuitable for the determination of the attributes/properties of theeyeglasses lens. To this end, typically in some embodiments of thepresent invention the image capturer 110 includes an auto image capturemodule 112 that is configured and operable to obtain an image of thereference object that satisfies certain prerequisites.

For example in some embodiments the auto image capture module 112verifies that the captured image satisfies such the prerequisite thatthe image includes tow image part showing two respective parts of thereference object—whereby the two parts include:

-   -   (i) a first part of the image shows a respective part of the        reference object as viewed/captured through a lens of the eye        glasses; and    -   (ii) another/second image part shows a respective part of the        reference object as viewed/captured directly (namely herein the        phrases captured/viewed directly should be understood as being        viewed/captured not through the lens of the eyeglasses).

To achieve that the auto image capture module 112 includes and autoimage selector module 114 that is configured and operable for receivingconsecutive preview images from the camera 196 (e.g. by utilizing aPreview Image retriever module connected to the camera). Upon receivingthe image capturing instruction from the user, the auto image capturemodule 112 operates the image selector module 114 to retrieve andprocess preview images from the camera. The image selector module 114(e.g. utilizing an Auto Image Framing module thereof) processes thepreview images to determine a preview image satisfying the requiredprerequisites as indicated above. in some embodiments the image selectormodule 114 (e.g. utilizing an Auto Image Framing module thereof) isconnectable to the image analyzer module and is adapted to provide thepreview image(s) to image analyze module 120 to determine whether theappearance of the reference object 196 in the preview image satisfiesthe required prerequisite. The operation of the image analyzer module120 in this regards is discussed in further detail with respect to theimage section analyzer 122 sub-module of the image analyzer module 120.Once a suitable preview image is identified, fulfilling all the requiredprerequisites, auto image capture module 112 operates the camera triggermodule to receive the image from the camera.

Turning now to the Image Analyzer 120 Analyzer 120, the Image Analyzer120 is adapted to process the image provided by the image capturer 110to determine/extract several parameters of the image from which theproperties, particularly filtration properties, of the lens of theeyeglasses can be determined.

As indicate above, in some embodiments of the present invention, theproperties of the eyeglasses lens are determined by comparing the firstand second parts of the image (the first showing a portion of referenceobject through the eyeglasses lens, and the second showing a portion ofreference object viewed directly). To this end, in some embodiments theImage Analyzer 120 includes an image section analyzer module 122 that isconfigured and operable for processing the image obtained by the imagecapturer 110 to extract/segment the image into the first and secondimage parts indicated above (one showing part of the reference objectthrough the eyeglasses lens, and the second showing a part of thereference object viewed directly).

In some embodiments, the image section analyzer module 122 includes aReference Object Recognizer module which is configured and operable toidentify the appearance of the reference object in the image. To thisend, in embodiments in which the reference object is a predefined objectregistered in data-storage/memory 185 (reference object(s) DB), theReference Object Recognizer module may include/utilize patternrecognition tools (e.g. neural network based or other) toidentify/recognize the appearance and location of the reference objectin the image based on the information (visual data) of the referenceobject that is stored in the reference object(s) DB 185. Alternatively,in cases where the reference object is not predefined (is arbitrary),the Reference Object Recognizer module may employ pattern recognitiontools to identify the largest/most homogeneous object that appears inthe image in part through the eyeglasses lens and in part directly.

Upon recognizing the reference object in the image the image sectionanalyzer module 122 crops the image to the extent of only the parts inwhich the reference object is shown and then segments the cropped imageto two parts: the first showing the reference object through the lensand the second not through the lens. The image section analyzer module122 may optionally include specifically adjusted edge detection module,referred to herein as Lens Edge Recognizer, which is configured andoperable to identify lens edges in the image. The Lens Edge Recognizermay be operable with similar edge detection techniques generally knownin the art, or other pattern recognition techniques, bytuning/configuring such techniques to the detection of the edges/frameof the eye glasses and/or for detection of edges of the lens itself(e.g. in the case of rimless frameless eyeglasses frames) glasses). Thenupon detection the image parts showing the reference object through thelens and directly, the image section analyzer module 122 (e.g. the imagesection splitter sub-module thereof) segments the cropped image in tothe first and second parts of the image IMG as follows:

-   -   Image-Part-I in which a first part of the reference object 196        appears through the lens; and    -   Image-Part-II in which a second part of the reference object 196        appears directly (not through lens).

The image analyzer 120 also includes an image verification module thatis configured and operable to process the reference image IMG,particularly the image parts Image-Part-I and Image-Part-II thereof todetermine whether a sufficient portion of the reference object 196 iscaptured in each of the image parts. To this end, a sufficient portionis considered to be a portion of the reference object which is largeenough or diverse enough in the content of the reference object appearstherein to allow reliable analysis and estimation of the properties ofthe lens. For instance, in case the reference object is a pattern (e.g.repetitive), the image verification module may be adapted to verify thateach of the image parts show at least one section of the backgroundobject (e.g. unit-cell/tile of the pattern thereof). In case thereference object should present a certain diversity/number of differentcolors, the image verification module may be adapted to verify that eachof the image parts show at least the required number/diversity of thecolors.

In this regards, as will be apparent from the description below, thediversity of colors in the reference object may be used to assess thespectral/chromatic filtration properties in the lens. In thisconnection, the specific technique for assessing whether the image partsImage-Part-I and Image-Part-II are suitable for further processing ofthe required lens' properties, may generally depend on the type of thereference object used and the type of properties. Such data may be forexample stored in the reference object data-base 185, to associated eachreference object with the types of lens properties for which it can beuse, and the kind of test to be conducted by the an image verificationmodule to determine whether image parts Image-Part-I and Image-Part-IIcapture sufficient parts/portions of the reference object fordetermining the lens properties.

Accordingly, based on the required lens properties, which are to beanalyzed (e.g. the spectral filtration properties of the lens the imageverification module determines whether the captured image partsImage-Part-I and Image-Part-II are suitable and sufficient.

As indicted above, in some embodiments the image section analyzer 122 ifoperated first on a preview image obtained from the auto image selector114, and once determining the preview image is suitable, providesinstructions data/signals to the auto image capturer 112 to grab thepreview image (e.g. with higher resolution) for further processing.

Additionally or alternatively, the image section Analyzer may operate onthe full resolution image IMG to determine that it is suitable or notand to segment it to the first and second parts Image-Part-I andImage-Part-II as discussed above.

System 1000 also includes a lens properties analyzer module 130 which isadapted to process the first and second parts Image-Part-I andImage-Part-II obtained by the image analyzer module 120 toidentify/estimate one or more properties of the lens.

To this end, as illustrated in the figure, the lens properties analyzermodule 130 is adapted to receive data indicative of the first and secondparts Image-Part-I and Image-Part-II from the image analyzer module 120.Optionally, the lens properties analyzer module 130 also received fromthe image analyzer module 120 data indicative of a first and secondparts Obj-Part-I and Obj-Part-II of the reference object 196 whichcorresponds to the visible parts of the object 196 in Image-Part-I andImage-Part-II. The first and second parts Obj-Part-I and Obj-Part-II ofthe reference object 196 may be extracted by the reference objectrecognizer module (being the sub-module of the image section analyzerdiscussed above) from the reference data in the data base 185, byanalyzing/recognizing the parts of the object 196 visible in theImage-Part-I and Image-Part-II.

The lens properties analyzer module 130 is configured and operable tocompare the appearance of the reference object on the first and secondparts of the image Image-Part-I and Image-Part-II, and thereby generatedata/signals indicative of one or more properties of the lens. The lensproperties data is determined based on the differences between theappearance of the relevant parts Obj-Part-I and Obj-Part-II of thereference object in the image parts Image-Part-I and Image-Part-II inwhich the reference object is viewed through the lens and directlyrespectively.

In various embodiments of the present invention the properties of thelens which are estimated include any of the following or both:

-   -   (i) Spectral filtration properties of the lens (e.g. filtration        properties of the lens itself—inherent to the lens material(s),        and/or filtration properties associated with lens coatings),        and/or    -   (ii) Defects, such as scratch/peeling of the lens.

In embodiments where the lens properties analyzer module 130 isconfigured and operable for determining the spectral filtrationproperties of the lens, it includes a Lens coating/filter analyzermodule 132. The Lens coating/filter analyzer module 132 is configuredand operable for processing the image parts Image-Part-I andImage-Part-II, to determine the spectral content/profile of similarsegments of the reference object appearing in those parts. The Lenscoating/filter analyzer module 132 includes a Lens Spectral ParameterExtractor sub module which determines/estimates spectral properties ofthe lens. To this end the Spectral Parameter Extractor sub module mayutilize the data about the relevant parts Obj-Part-I and Obj-Part-II ofthe object appearing in the Image-Part-I and Image-Part-II to identifyon those image parts sections pertaining to similar segments of thereference object 196. The spectral content/profiles may be for examplethe average intensity values of the primary colors (e.g. of RGB or otherpallet) extracted from the similar parts of the reference objectappearing in the first and second image parts Image-Part-I andImage-Part-II. Then the difference (referred to hereinafter also asspectral difference profile) between the first and spectral profiles ofrespectively the first and second image parts Image-Part-I andImage-Part-II, is determined/calculated. This spectral differenceactually represents the degree of attenuation of light of differentcolors through the lens. Accordingly filtration properties of the lensare identified/extracted by the Lens coating/filter analyzer module 132.This data may then be presented to the user as a result of the lens testcarried by the system.

In some embodiments the Lens coating/filter analyzer module 132 alsoincludes a filter classifier module (F-Classifier in the figure) whichis configured and operable to process the spectral difference profile todetermine/classify the profile to any one or more of know/conventionalfilters used in lenses and/or to identify it as unique filter. To thisend the F-Classifier may include or be associated with data-storagestoring the spectral attenuation parameters of one or more (typicallyplurality) of conventional lens filters/coatings. The F-Classifier maydetermine whether there is a match between the spectral differenceprofile and the spectral attenuation profiles of one (or possiblymore—combination of several) known lens filter and upon determining amatch—identifying that the lens is associated with such one (orcombination) of filters. This data may then be presented to the user asa result of the lens test carried by the system.

Thus, as described above in order to determine filtration properties ofthe lens, the spectral content of the first and second parts of theimage, Image-Part-I and Image-Part-II, are compared so as to identifydifference(s) between the spectral content of the reference objectviewed through the lens and directly, and thereby estimate the spectralparameters of the filter/coating provided by the lens. However, theinventors have noted that in some cases an non-balanced spectral contentof the image IMG captured by the imager 192, may distorted/affect theresult of such analysis. For instance, such non balanced spectralcontent may be as a result of purely tuned white balance (WB)operational parameter of the imager 192, or due to deviation of theambient lighting of the environment at which the reference object iscaptured from the “neutral” white light (e.g. or parley tunedWB/“temperature” setting of the display screen 194, by which thereference object 196 may be presented. This may be performed for exampleby a display parameters adjuster module 184 (e.g. displaydriver/controller) which may be included in the system 1000 of thepresent invention and configured and operable for adjusting parametersof the display 194. In the following for clarity, any/all of theseeffects are referred to as the un-balanced WB parameters of the imageIMG.

Therefore, in some embodiments there is a need to identify theun-balanced WB parameters of the image IMG and to be able to compensatefor them, before performing analysis of the spectral filtrationparameters of the lens. To achieve that the system 1000 (the imageanalyzer 122) may include a spectral image analyzer module 124configured and operable to identify the un-balanced WB parameters of theimage IMG (indicated WB params in the figured). In some implementationsthe spectral image analyzer module 124 receives the spectrally balancedimagery data of the reference object from the reference object DB 185(which may store such data about any reference object stored thereby)and compares that with the spectral information from the second part ofthe image, Image-Part-II (which is captured directly—not through thelens). Then the un-balanced WB parameters of the image IMG aredetermined in accordance with the deviation between the portions of thespectrally balanced imagery data and the spectral information of theImage-Part-II which pertain to the similar region of the referenceobject 196. Alternatively or additionally, the un-balanced WB parametersof the image IMG can be determined by processing the image spectrum toclassifying the image IMG to any of one or more conventional ambientlighting scenarios (e.g. based on conventional light temperatureclassifications: Warm Light e.g. Tungsten Lamp, White Light e.g. daylight, Cold Light, e.g. Screen/Blue light, etc.), and/or in case thereference object is presented on screen 194, utilizing input dataindicative of the operation light temperature parameter of the screen194 by which the reference object displayed.

To this end, in some embodiments the Lens coating/filter analyzer module132 also includes a spectral calibration module which is configured andoperable for receiving the un-balanced WB parameters (WB params) fromthe spectral image analyzer module 124 and calibrate/adjust the colorsof the image IMG (e.g. apply similar color adjustment to both imageparts Image-Part-I and Image-Part-II) in accordance with the WB paramsso as to compensate the effects of the un-balanced WB parameters (e.g.in such manner the WB in the second image part Image-Part-II should beoptimally neutralized or at least partially compensated). In suchembodiments the operations described above with reference to the LensSpectral Parameter Extractor are performed only after the un-balanced WBparameters are compensated for by the spectral calibration module.

In embodiments where the lens properties analyzer module 130 isconfigured and operable for determining the defects, such as scratches,peelings and/or cracks in the lens, it includes a Lens defects analyzermodule 135. The Lens defects analyzer module 135 is configured andoperable for processing the image parts Image-Part-I and possibly alsoImage-Part-II, to determine defects of the lens affecting the appearanceof the reference object appearing in first part of the imageImage-Part-I. The defects may include one or more scratches and/orpeelings and/or cracks affecting blurriness of the reference object'part being viewed in the first part of the image Image-Part-I.Accordingly the Lens defects analyzer module 135 may be for exampleadapted to process the first and possibly second parts of the image todetermine sharpness level of the first image part Image-Part-I andpossibly also the second image part Image-Part-II and upon determiningthe sharpness level of the first image part Image-Part-I is below acertain sharpness threshold and/or optionally upon determining that thedifference between the sharpness levels of the first and second imageparts if above a certain threshold, issue/generate data indicative ofthe lens being defective. Sharpness level may be determined globally orlocally according to any known in the art technique or other technique,for example by obtaining a spatial derivative(s) of each image part(e.g. Image-Part-I and possibly Image-Part-II) for whichsharpness/blurriness degree should be estimated) and determining thesharpness degree based on the local and/or global average of thederivative of the respective image part (whereby higher averageindicates sharper—less blurry image and vice-versa).

It should be noted that in some embodiment the reference object 196 is aphysical object 196.1, while on some embodiments it may be virtualobject 196.2 presented on display screen 194. To this end, in someembodiments the system includes a virtual reference generator module180, connectable to a reference object database 185, and configured andoperable for communicating (e.g. by wired or wireless communication)with the display screen 194 for presenting a selected reference objecton the screen.

It should be noted that the apparatus/system 1000 for testing ofeyeglasses using a background object, according to an exemplaryembodiment of the present invention is implemented on a computer. Thecomputer may actually be one or more computers, as described in furtherdetail hereinbelow. Optionally, the computer may include a user'scomputer such as a mobile phone, a tablet computer, or another computerin use by a user, a computer in remote communication with the user'scomputer, etc., or any combination thereof, as described in furtherdetail hereinbelow. Thus, optionally, all parts of the apparatus 1000are implemented on the user's computer, say on a mobile phone, tabletcomputer, laptop computer, or other computer in use by the user, asdescribed in further detail hereinbelow. Optionally, one or more of theapparatus 1000 parts (say one or more of the parts 110-130 shown in FIG.1A) may be implemented at least in part, on a remote computer incommunication with the user's computer, as described in further detailhereinbelow. The apparatus/system 1000 may include or be implemented forexecution on at least one computer processor, say one or more computerprocessor of the user's computer (say the user's smart mobile phone),one or more computer processors of the remote server, or both. Theapparatus 1000 further includes one or more additional parts describedin further detail hereinbelow, such as the parts denoted 110-130 in FIG.1A.

The additional parts may be implemented as software—say by programmingthe one or more computer processors to execute the method described infurther detail hereinbelow and illustrated using FIG. 2A, ashardware—say as an electric circuit that implements at least a part themethod, etc., or any combination thereof.

In one example, one or more parts of the apparatus 1000 is implementedas a computer application such as iPhone® App, which may be downloadedto the user's smart cellular phone (say an Apple® iPhone) or tablecomputer (say an Apple® iPad), as described in further detailhereinbelow. Reference is now made to FIG. 1B, which is a block diagramschematically illustrating a first exemplary system/apparatus 1000 fortesting of eyeglasses according to an embodiment of the presentinvention by utilizing a background object.

In the present example system/apparatus 1000 includes an imagecapturer/provider module 110 configured and operable forproviding/capturing one or more stills or video images of a backgroundobject, an image analyzer module 120 being in communication with theimage capturer 110, and configured and operable for analyzing the imageprovided by the image capturer 110, as described in further detailhereinbelow. The system/apparatus 1000 also includes a propertyidentifier 130 in communication with the image analyzer 120. Theproperty identifier 130 identifies/determines/estimates one or moreproperties of the lens installed on the eyeglasses based on the analysisof the image by the image analyzer 120 (e.g. based on the analysis ofthe color characteristics of the image).

The background object may be physical object such as a human face, apattern presented on a screen (say a computer screen, a screen or paperon which the pattern is projected from a slide projector, etc.), apattern printed on a physical object (say on paper), and/or a virtualobject (e.g. a pattern/image) presented on a display (screen).

Optionally, the background object is a predefined pattern—say a patternpredefined by a programmer or an operator of apparatus/system 1000.

Optionally, the image capturer 110 captures the image using an imagecapture device (imager/camera e.g. a mobile phone's camera) in use bythe user, as described in further detail hereinbelow.

The image capturer 110 may be implemented, for example, on the user'sdevice/computer, on a remote computer in communication with the user'sdevice/computer or with the image capture device, or on both computers,as described in further detail hereinabove.

Optionally, the image capture device is a camera installed on a mobiledevice/phone in use by the user, and the image capturer 110 controls thecamera, say by using the camera to capture images (say a video stream orseveral stills images), by forwarding one or more of the images foranalysis, etc., as described in further detail hereinbelow. In otherexamples, the image capture device may be a camera installed on a tablecomputer, on a laptop computer, or on another computer, similarlycontrolled by the image capturer 110, as described in further detailhereinbelow.

The image capturer 110 captures at least a part of the background objectthrough a lens of a pair of eyeglasses. The image capturer 110 maycapture one or more remaining parts (if any) of the background objectnot through the lens, as described in further detail hereinbelow, and asillustrated, for example, in FIG. 9.

Thus, in a first example, a background object in a form of a predefinedpattern is presented on a screen, say an LCD (Liquid Crystal Display)Screen, as known in the art. Simultaneously to presentation of thepredefined pattern on the digital screen, a user holds his eyeglassesopposite the screen, or rather places the eyeglasses on a surface (say adesktop) opposite the screen.

Then, the image capturer 110 captures the predefined pattern in animage, such that one part of the pattern is captured through one of theeyeglasses' lenses. The remaining part of the pattern extends out of thearea behind the lens, and is thus captured directly—i.e. not through thelens.

As indicated above the apparatus 1000 further includes an image analyzer120 in communication with the image capturer 110. The image analyzer 120analyzes the image captured by the image capturer 110, as described infurther detail hereinbelow, and as illustrated, for example, in FIG. 2A.

Optionally, the image analyzer 120 analyzes the image's colorcharacteristics. For example, the image may be a digital color imagebased on the RGB color model, as known in the art, and the imageanalyzer 120 may analyze the image by comparing RGB color channelintensity values measured over the background object as captured in theimage, as described in further detail hereinbelow.

In a first example, the image analyzer 120 compares between one or moresame color channel intensity values (say mean or sum intensity values,each of which values is calculated over all pixels of a respective oneof one or more of the R.G.B. color channels of the image) of two partsof the image in which the background object is respectively captured inthe image which being viewed through the lens of the eye glasses (firstpart of the image) and being viewed directly—not though the lens of theeyeglasses (second part of the image).

Indeed, the background object may include multiple sections havingdifferent color contents. It should be understood that when comparingthe color contents of the two parts of the image in which the backgroundobject is captured through the lens and directly, the analyzer actuallyanalyzes to appearance of the object in the two parts of the image toidentify the two parts of the image, in which appearing sectionsbackground object which have the same color content in the object.Accordingly the analyzer may segment the image to isolate/extractsegments at which the similar sections of the object (with similar colorcontent of the object) appear in the image whereby in the first part thesection appears while being viewed through the lens, and in the secondpart, it appears while being viewed directly (not through the lens). Tothis end, the sections of the similar/same color content of the object,may be monochromatic sections (e.g. white color, blue color skin coloror other) or may include a pattern of multiple colors whereby thesimilar colors are shown in the two parts of the image. Accordingly theanalyzer 120 segments/extracts two parts from the image which showrespective parts of the object having the similar color contents,whereby one part is viewed from through the eyeglasses lens and theother directly.

To this end, in the first example, the two parts of the object, whichare captured through-the-lens and directly, respectively, are supposedto have the same color pattern/content. For example, the backgroundobject may be a predefined pattern, and the two parts of the pattern mayhave the same color content(s), e.g. two parts of one of the slices ofthe radial pattern illustrated in FIG. 5 hereinbelow. Similarly, thebackground object may be a face of a person—say of a user who wears theeyeglasses, and in that case too, both parts are supposed to have asame/similar color content—namely, the person's skin color.

However, in the image, in which the first part is captured through thelens, and the second/remaining part is captured directly—i.e. notthrough the lens, the color content of the first and second parts may bedifferent due to spectral filtration effect of the lens (this is unlessthe lens is neutral).

To this end, in some embodiments the image analyzer 120 compares thedifferent color channels in between the first and second parts of theimage in which first and second parts of the object appear, so as todetermine the filtration parameters of the lens. In the example in whichR.G.B. image is used, one comparison may be performed between bluechannel intensity values of the two parts and/or another comparison maybe performed between red channel intensity values of the two partsand/or possibly another comparison another comparison may be performedbetween the green channel intensity values of the two parts. Accordinglyother comparison may be performed in cases where the image is providedin other color pallets (e.g. such as CMKY, or L*a*b color space)

The comparison is thus used to assess a per-color-channel (i.e. perprimary color) difference between the part of the background objectcaptured through the lens and the part of the background object patternthat is rather captured not through the lens.

In a second example, the image analyzer 120 compares between absolutedifferences or ratios, calculated between different color channelintensity values (say mean or a sum intensity values, each of whichvalues is calculated over all pixels of a respective one of one or moreof the channels). The absolute differences or ratios are calculated fortwo different parts of the background object as captured in theimage—say for a part captured through the lens, and for a part captureddirectly (i.e. not through the lens). The comparison is thus used toassess how the two parts differ in as far as the ratio or absolutedifference between different primary colors in each part is concerned.

In a third example, the image analyzer 120 compares between one or morecolor channel intensity values (say a mean or a sum intensity valuecalculated over all pixels of each respective one of one or more of thechannels) and a reference value predefined per each respective one ofone or more of the channels. The one or more color channel intensityvalues may be calculated for the whole background object as captured inthe image, or rather per each one of one or more different parts of thebackground object as captured in the image. Similarly and respectively,the one or more reference values may be defined for the whole backgroundobject or rather per each one of the one or more color sections of thebackground object. The comparison is thus used to assess a pre-channel(i.e. per primary color) deviation from the predefined reference values.

In a fourth example, the image analyzer 120 calculates one or more colorchannel intensity values (say a mean or a sum intensity value calculatedover all pixels of each respective one of one or more of the channels).Again, the one or more color channel intensity values may be calculatedfor the whole background object as captured in the two parts of theimage, or rather per each one of one or more different sections (ofdifferent color contents) of the background object as captured in thetwo parts of the image.

In the fourth example, the image analyzer 120 further calculatesabsolute differences or ratios between the color channel intensityvalues. Then, the image analyzer 120 compares the differences or ratiosto reference values predefined for the whole background object or ratherper each one of the one or more parts of the background object.

The comparison is thus used to assess the deviation of the wholebackground object as captured in the image, or rather of one or morespecific parts of the captured background object from a predefined ratioor absolute difference between primary colors.

Optionally, in the fourth example, there are further compared thedeviations from the predefined ratios or absolute differences, between apart of the background object that is captured through the lens and apart of the background object that is captured directly (i.e. notthrough the lens).

Optionally, the apparatus 1000 further includes a reference valuecalculator (not shown) in communication with the image analyzer 120.Optionally, the background object is a predefined pattern and thereference value calculator calculates the one or more reference valuesin a preliminary step. Optionally, in the preliminary step, thereference value calculator calculates one or more reference values pereach specific one of the predefined pattern's sections based on thespecific section's coloring in the predefined pattern, as described infurther detail hereinbelow. The calculation may be based, for example,on an averaging or summing over intensity values expected for thatsection upon presentation on a screen of a specific model, in light ofthe screen's technical characteristics and the color selected for thatsection, as described in further detail hereinbelow. Optionally, thecolor is selected for the section based on the screen's technicalcharacteristics—say base on technical characteristics of the screen asinput by an operator or programmer of apparatus 1000, as described infurther detail hereinbelow.

Additionally or alternatively, in the preliminary step, the referencevalue calculator allows one or more of the reference values to bedefined by an operator or programmer of the apparatus 1000, say using aGUI, as described in further detail hereinbelow.

As indicated above, the property identifier 130 identifies a property ofthe lens installed on the eyeglasses based on the analysis of the imageby the image analyzer 120, say based on the analysis of the colorcharacteristics of the image.

Optionally, the property identifier 130 identifies the property based onthe image analysis carried out using the color channel intensity values,say using one or more of the comparisons illustrated using the examplesprovided hereinabove, as also described in further detail hereinbelow.

Additionally or alternatively, the analyzing by the image analyzer 120includes using the RGB intensity values color channel (e.g. theintensities of the RGB color channels of the two parts of the object)measured over the area of the background object as captured in the twoparts of the image, or rather over specific sections of the backgroundobject captured in the two parts of the image, to calculate a lighttransmittance spectrum of the eyeglasses lens (say data indicative of aspectral graph of the lens), as described in further detail hereinbelow.Subsequently, the image analyzer 120 compares the calculated spectrum toa reference spectrum expected for the whole background object or for thespecific parts. Then, the property identifier 130 may identify aproperty of the lens, using a result of the comparison of the spectrums,as described in further detail hereinbelow.

Thus, in one example, the background object is a predefined patternpresented on a computer screen standing behind the eyeglasses. In theexample, the image analyzer 120 may calculate the reference spectrum forthe whole predefined pattern or rather per each specific one of one ormore of the pattern's sections. The image analyzer 120 calculates thereference spectrum based on coloring in the predefined pattern and oncharacteristics of a screen in use for presenting the pattern, asdescribed in further detail hereinbelow.

Optionally, the image analyzer 120 further compares between a pair ofspectrums within a predefined wavelength range—say within a wavelengthrange typical of visible light (say of about 400-700 NM). One of thepair spectrums is a spectrum calculated based on color channelsintensity values measured over a part of image showing the part of thebackground object that is captured through the lens. A second one of thespectrums is a spectrum calculated based on color channel intensityvalues (e.g. RGB channels) measured over a part of the image showing thepart of the background object that is captured directly (i.e. notthrough the lens).

Optionally, the image analyzer 120 may further identify a deformationalong a segment within the background object—say the predefined pattern,say along a virtual linear segment which extends between two pointsinside a blank part of the predefined pattern as captured in the image.The two points may be selected arbitrarily or rather so as to examineone or more specific regions of interest and for identifying adifference between the regions. For example, a region of interest (ROI)may be an area in the middle of the background object as captured, anarea around the center of the lens, a part partially captured throughthe lens and partially captured not though the lens, etc. as describedin further detail hereinbelow.

The deformation may include, but is not limited to: an unexpecteddiscontinuity of the virtual segment, a change in curvature of thevirtual segment (say an unexpected sinusoidal form when expected to bestraight), etc., as described in further detail hereinbelow.

Optionally, the image analyzer 120 may further identify a difference insharpness between a part of the background object captured through thelens and a part o the background object captured directly (i.e. notthrough the lens). The sharpness may be calculated, for example, usingAbsolute Central Moments, Diagonal Laplacians, etc., as described infurther detail hereinbelow.

Subsequently, based on the deformation, sharpness difference, or both,as identified by the image analyzer 120, the property identifier 130identifies a property of the lens, as described in further detailhereinbelow.

Optionally, the image analyzer 120 uses one or more functions based onone or more of the above described comparisons identifying of thedeformation and identifying of the difference in sharpness.

In one example, each one of the functions yields an indicator usable foridentifying a different, respective property of the lens.

Optionally, the property identified by the property identifier 130 is aone of having a predefined coating, a predefined filter, or of havingboth the coating and the filter, as described in further detailhereinbelow.

Optionally, the property identified by the property identifier 130 is aone of having a High Energy Visible (HEV) light protective coating, aHEV light protective filter, or both the HEV light protective coatingand the HEV light protective filter.

Optionally, the property identifier 130 identifies the property ofhaving the HEV protective coating, filter, or both, based on asignificantly lower mean of pixel intensity values in the RGB Bluechannel in parts of the background object captured through the lens whencompared to parts captured directly (i.e. not through the lens).

Thus, in one example, the property identifier 130 identifies the lensproperty of having the HEV protective coating, filter, or both, based ona comparison made between parts of the background object that are knownto have a same color (say parts of a background object, the whole ofwhich object is painted with a same color).

In the example, the property is identified when the mean of pixelintensity values in the RGB Blue channel in a part of the backgroundobject captured through the lens is at least 10% lower than in thebackground object's part captured directly (i.e. not through the lens).

Optionally, the image analyzer 120 calculates the means over all pixelsin an area of the image occupied by the background object (say apredefined pattern presented on a screen or on paper), or ratherspecifically per each one of one or more parts of the area occupied bythe background object, over all pixels in that part, as described infurther detail hereinbelow.

Thus, in one example, the background object is a predefined pattern thatis white and blank, and the image analyzer 120 calculates the meanintensity values for the whole area occupied by the predefined pattern,in the RGB Red and Blue channels. In the example, the image analyzer 120calculates the mean for each one of the two channels, and rounds themean to the nearest integer.

In the example, when the ratio of the rounded mean intensity value ofthe Blue channel to the rounded mean intensity value of the Red channelis below 0.90, the property identifier 130 identifies the property ofhaving a HEV coating, HEV filter, or both.

Optionally, the property identified by the property identifier 130 is aone of an effective Anti-Reflection (AR) coating or filter.

Optionally, the image identifier 130 identifies that the lens has aneffective Anti-Reflection (AR) coating or filter, based on a reductionof less than 3% in the mean intensity value of each one of the RGBchannels when compared to predefined reference values, as described infurther detail hereinbelow.

Optionally, the image identifier 130 identifies that the lens ahs aneffective Anti-Reflection (AR) coating or filter, based on a less than3% reduction in the mean intensity value of each one of the RGB channelsin a part of the background object that is captured through the lenswhen compared to a part that is known to have a same color, but iscaptured directly (i.e. not through the lens).

The image analyzer 120 may calculate the mean intensity values per thewhole area of the image occupied by the background object (saypredefined pattern), or rather specifically per each one of one or moreparts of the image area occupied by the background object, as describedin further detail hereinbelow.

Optionally, the property identified by the property identifier 130pertains to a driving compatibility of the lens.

The identifying of the property which pertains to the drivingcompatibility of the lens may be based on one or more requirements, asspecified, for example, in the ISO (International Organization forStandardization) 8980-3 2013 standard.

A key concern when identifying a driving compatibility of a lens of apair of eyeglasses is a driver's ability to tell one color from theother, when viewed through the lens.

For example, when light in a certain primary color is not transmittedfully through the lens, there may be distorted the color as capturedthrough the lens. Consequently, the driver may be unable to tell onetraffic light from another.

Thus, in one example made with reference being diverted to FIG. 5, theidentifying of the driving compatibility property is base don the radialpattern 5000 illustrated using FIG. 5 hereinbelow.

In the example, each one of the pattern's 5000 parts/segments (alsoreferred to hereinbelow as slices) 501-512 arranged around the centralwhite area 500 is designed with a different color, as described infurther detail hereinbelow.

Further in the example, when designing the pattern, it is made sure thatfor each one of the segments 501-512, a mean intensity value calculatedfor at least one of the RGB channels would differ in at least 40% from amean intensity value calculated for each one of the other parts 501-512,for that channel.

Optionally, the mean intensity values are re-calculated over each part's501-512 part that is distal with respect to the center 500 of thepattern as captured by the image capturer 110, which distal part iscaptured directly (i.e. not through the lens), as illustrated in FIG. 5.Then, the difference of at least 40% among the mean intensity valuescalculated for the different parts 501-512 for the channel is confirmed.

In the example, if for each and every one of the parts 501-512 ascaptured through the lens, a mean intensity value calculated for atleast one of the RGB channels differs in at least 20% from a meanintensity value calculated for each one of the other parts 501-512, forthe channel, there is identified that the lens fits driving. The meanintensity values are calculated over the part's part that is proximalwith respect to the center 500 of the pattern, which proximal part iscaptured through the lens, as illustrated in FIG. 5

Optionally, the image analyzer 120 may further identify a predefineddeformation along a segment within the background object—say thepredefined pattern, say along a virtual linear segment which extendsbetween two points inside a blank part of the predefined pattern, asdescribed in further detail hereinabove.

Based on the identified deformation of the segment, the propertyidentifier 130 may identify one or more properties of the lens (say ofhaving a scratch, crack, or peeling), as described in further detailhereinbelow.

Optionally, the property identifier 130 further identifies one or moreother properties of the lens, say using one or more parts of the methodsteps of analysis 220 (which may be carried out by the image analyzer110) and identifying 230 as described in further detail hereinbelow.

Optionally, the property identifier 130 further identifies a propertythat has to do with opacity of the lens, say by using one or more of theproperties mentioned hereinabove to roughly estimate the opacity of thelens. Thus, in one example, the property identifier 130 may estimate theopacity based on a count of the number of scratches or cracks identifiedon the lens, as described in further detail hereinbelow.

Optionally, the apparatus 1000 further includes a pattern projector (notshown).

Optionally, the pattern projector presents the predefined pattern on ascreen (say a computer screen, a mobile phone screen, etc.).

Optionally, the pattern projector projects the predefined pattern onto asurface such as a paper sheet, etc., as known in the art. Optionally,the pattern projector projects the predefined pattern from a lightsource such as a slide projector, a computer screen, a screen of amobile phone, a flashlight, an indoor or other light (say through aslide which bears the pattern), etc.

Optionally, in order to enable identifying of a property of a coating onthe lens independently of the lens side (frontal side or back side)being coated, the image analyzer 120 uses both an image captured withthe lens frontal side facing the light source and an image captured withthe lens back side facing the light source.

The pattern projector may be implemented, for example, on a firstcomputer (say the user's computer), on a remote computer incommunication with the first computer or with the light source, or onboth computers, as descried in further detail hereinabove.

Thus, in a first example, in a preliminary step, the image projectorpresents the predefined pattern (i.e. the background object of theexample) on a screen of a laptop computer.

Optionally, in the first example, a user places a pair of eyeglasses ona surface such a desktop, such that at least a part of the presentedpattern can be captured through a lens of the eyeglasses, simultaneouslyto presentation of the pattern on the screen.

The, the image capturer 110 captures an image of the predefined pattern.

In the first example, the image capturer 110 captures the predefinedpattern (i.e. the background object of the instant example) in theimage, say using a camera of a mobile phone (say the user' smart phone),on which phone, in the example, the apparatus 1000 is implemented, asdescribed in further detail hereinbelow.

Optionally, on the background object (say on the presented predefinedpattern) there appears a marking usable by a user for aligning theeyeglasses in a predefined orientation prior to capturing of thebackground object in the image, as described in further detailhereinbelow, and as illustrated, for example, in FIG. 5 and FIG. 6.

Optionally, the apparatus 1000 further includes a color selector.

The color selector carries out a preliminary color selection step priorto presentation of the predefined pattern, say prior to the presentationof the predefined pattern on a digital screen such as a laptop or tabletcomputer screen or a mobile phone screen, as described in further detailhereinbelow.

In the color selection step, the color selection for one or more partsof the predefined pattern is optimized.

The color selection may be optimized according to technicalcharacteristics of the image capture device—say a digital cameraintended to be used for capturing the image, of a device intended to beused for presenting the pattern—say a digital screen, or of both device,as described in further detail hereinbelow.

For example, the color selection may be optimized according to intensityof color emission by the screen per different colors, according tosensitivity of the camera to light in different primary colors, etc., asdescribed in further detail hereinbelow and as illustrated for example,using FIG. 3-5.

The predefined pattern may be designed in various ways.

For example, the predefined pattern may be colored, non-colored (saygrayscale), or rather have one or more colored parts and one or morenon-colored (say grayscale) parts.

Optionally, the predefined pattern may be blank, non-blank (say a onewith graphical or textual content), or rather have one or more blankparts and one or more non-blank parts.

Optionally, the predefined pattern includes two or more parts ofdifferent color, color level (say grey level) or texture, and each parthas a respective, predefined color, color level or texture, and arespective, predefined position within the pattern.

Consequently, the image analyzer 120 may analyze the predefined patternas captured in the image based on the color, color level or texture ofat least one of the parts, and on the part's position within thepredefined pattern, say using one or more of the above describedcomparisons, as described in further detail hereinbelow.

Optionally, the background object (say the predefined pattern) includesone or more predefined markings.

In one example, a marking 550 extends rightward from the center of aradial pattern 5000, as illustrated, for example in FIG. 5.

In other examples, the markings may include, for example, a number, anarrow, a cross, a line, words, letters, etc., as descried in furtherdetail hereinbelow, and as illustrated, for example in FIG. 6.

Optionally, one or more of the markings may be used by the user inaligning the image capture device, the pair of eyeglasses, the screen,etc., in a predefined way, as described in further detail hereinbelow.

Optionally, the apparatus 1000 further includes an orientationdeterminer (not shown).

The orientation determiner automatically identifies and locates themarking on the background object (say the predefined pattern) ascaptured in the image.

Once identifying and locating the marking, the orientation determineruses the markings to identify an orientation of the background object ascaptured in the image, to identify the background object's center, etc.,as described in further detail hereinbelow.

Optionally, the identified orientation, center, or both, is used by theimage analyzer 120 for analyzing the image, as described in furtherdetail hereinbelow.

Optionally, upon identified alignment of the background object (say thepattern) in a predefined orientation, the image capturer 110automatically forwards the image to the image analyzer 120, therebyinitiating the analyzing of the image by the image analyzer 120, asdescribed in further detail hereinbelow.

Optionally, only upon identified alignment in the predefinedorientation, does the image capturer 110 forward the image to the imageanalyzer 120, thereby initiating the image's analysis by the imageanalyzer 120, as described in further detail hereinbelow.

Optionally, the orientation determiner rather identifies the orientationof the background object based on a directional aspect of the texture ofthe background object (say of the predefined pattern), as described infurther detail hereinbelow, and as illustrated for example, in FIG. 7.

Additionally or alternatively, the orientation determiner further uses aGUI, for allowing the user to manually identify the center of thebackground object as captured in the image, the orientation of thecaptured background object, or both, say by manually marking the centeror orientation, as described in further detail hereinbelow.

Thus, in one example, the GUI used by the orientation determiner isimplemented using a screen of the user's mobile phone or other computer.In the example, real time video images of the background object ascaptured at least partially through the lens, using the image capturedevice of the mobile phone or table computer, are presented on thescreen.

In the example, simultaneously to presentation of the video images, theuser is allowed to mark the center by touching the screen, mark anorientation of the background object using a multi-touch gesture inwhich the user moves two fingers in a predefined way while touching thescreen, etc., as known in the art of multi-touch gesture recognition.

Optionally, the apparatus 1000 further includes a user guidance manager.

The user guidance manager guides the user through one or more stepsleading to the identifying of one or more properties of the lens.

Optionally, the identified orientation, center, or both, is used by theuser guidance manager for guiding the user in aligning the eyeglassesand image capture device (say by moving his mobile phone) in a specificorientation with respect to each other, as described in further detailhereinbelow.

Thus, in one example, the user guidance manager guides the user to movethe mobile phone over the eyeglasses, with the phone's camera facing theeyeglasses and background object, to the right, to the left, etc., untilthe background object as captured, aligns in a predefined orientation,as described in further detail hereinbelow.

The user may be guided for example, using vocal instructions given onthe mobile phone's speaker, using a GUI implemented using titles withinstructions, arrows, etc. or other cures that appear on the videoimages captured and presented on the phone's screen as the user movesthe mobile phone over the eyeglasses, etc.

Optionally, the orientation determiner further locates one or moreboundaries of the lenses of the eyeglasses, say using Canny EdgeDetection, an analysis of Oriented Gabor Filter Responses, etc., or anycombination thereof, as described in further detail hereinbelow.

Optionally, the user guidance manager uses the located one or moreboundaries for guiding the user in aligning the image capture device(say the phone's camera) and the eyeglasses in a preferred position withrespect to each other, such that the background object is captured in apredefined orientation.

For example, the user may be guide to align the image capture device andthe eyeglasses, so as to have a specific part of the background objectsas captured in the image, extend over two sides of the boundary, suchthe part is captured partially through the lens, and partially directly(i.e. not through the lens), as described in further detail hereinbelow.

Optionally, the orientation determiner further estimates location of thelens center, as described in further detail hereinbelow.

The estimating may be based, for example, on a mass center of the lensas calculated based on the location of the one or more boundaries, onintersection of a virtual vertical line positioned where the height ofthe lens is maximal with a virtual horizontal line positioned where thewidth of the lens is maximal, etc., or any combination thereof. Thehorizontality and verticality of the lines may be selected, for example,so as to be parallel to the horizontal side and vertical side of asmallest rectangle that would bound the located boundaries of lens, asknown in the art.

Optionally, the location of the lens center may be used by the imageanalyzer 120, for analyzing the image, say for looking for certaincurvature changes when expected on the lens center.

Optionally, the user guidance manager uses the estimated location of thelens center for guiding the user in aligning the image capture deviceand the eyeglasses in a predefined orientation with respect to eachother, such that a specific part of the background object is capturedthrough a preferable area of the lens, as described in further detailhereinbelow.

Optionally, the orientation determiner further locates a center of thebackground object, say the round blank area in the center of the patternillustrated using FIG. 5, say using known in the at image recognitiontechniques, as described in further detail hereinbelow.

Optionally, the location of the background object's center may be usedby the image analyzer 120, for analyzing the image, say for finding acertain feature of a known position with respect to the backgroundobject's center.

Optionally, the user guidance manager uses the located center of thebackground object for guiding the user in aligning the image capturedevice and the eyeglasses in a predefined position with respect to eachother, as described in further detail hereinbelow.

Thus, in one example, the user guidance manager uses a GUI implementedusing live video images captured by the user's mobile phone video cameraas the user moves the mobile phone over the eyeglasses. The GUI presentsthe capture video images to the user in real time, as the user moves themobile phone over the eyeglasses.

In the GUI, when the orientation determiner determines that the locatedbackground object center and the estimated lens center location in theimages, are close enough, say within a predefined distance from eachother (say a distance of less than 1% of the width of each one of thevideo images), the lens edges are colored green. However, when theorientation determiner determines that the located center of thebackground object and the estimated location of the lens are not closeenough, the lens edges are colored red.

Further in the example, when the edges are colored green, the imagecapturer 110 forwards the last image captured using the phone's videocamera for the image analyzer 120, thus initiating the analysis of theimage by the image analyzer 120.

However, when the edges are colored red, the user guidance managerpresents an arrow in the GUI, thereby guiding the user to move the phoneor eyeglasses in a direction necessary for aligning the phone's videocamera and the eyeglasses in a preferred orientation with respect toeach other. Optionally, the preferred orientation is predefined by anoperator or programmer of apparatus 1000, say using a GUI.

Optionally, the apparatus 1000 further includes a face featureidentifier.

The face feature identifier identifies one or more facial features (sayfeatures of a human face) in one or more of the captured images, sayusing known in the art face detection methods applied on the capturedimages (say the video images), as described in further detailhereinbelow.

Thus, in one example, the background object is a human face captured inthe image partially through the lens and partially directly (i.e. notthrough the lens). In the example, the face feature identifieridentifies the face and a specific feature thereof (say a nose or apupil). The face feature may be used in the analysis by the imageanalyzer 120 or in guiding the user (say to align the eyeglasses andimage capture device in a predefined, preferred orientation with respectto each other), etc., as described in further detail hereinabove.

Optionally, the property identifier 130 further identifies an assemblyquality of the eyeglasses based on the analysis by the image analyzer120.

For example, when the eyeglasses quality of assembly is low, pressureapplied on the lens by the eyeglass frame may have an effect on lighttransmittance in lens areas affected by the pressure. The areas affectedby the pressure are thus expected to differ in their light transmittancewhen compared to light transmittance through parts of the lens that arenot affected by that pressure.

Thus, in one example, the image analyzer 120 identifies that certainlens areas along the boundaries of the lens as identified (say by theorientation determiner) appear to have a mean RGB channel intensityvalue that deviates significantly (say in more than 10%) from mean RGBchannel intensity values of the lens' other areas.

Since lenses are typically engaged by the eyeglass frame along parts ofthe lens boundaries, the property identifier 130 may thus identify anassembly quality of the eyeglasses based on that analysis by the imageanalyzer 120.

Thus, in one example, when the deviation of the RGB channel intensityvalues is lower than a reference value (say a threshold of 10%) aspredefined by a user or programmer of apparatus 1000, the propertyidentifier 130 determines that the quality of assembly is high.

However, in the example, when the deviation of the RGB channel intensityvalues in certain areas along the identified boundaries is higher thanthe reference value (say the threshold), the property identifier 130determines that there is a problem in the quality of assembly.Optionally, the property identifier 130 further shows the parts of theframe that may have assembly problems to the user, using a GUI, say bymarking the parts of the lens boundaries along which the deviation ofthe RGB channel intensity values is higher than the reference value, inred color.

Optionally, the apparatus 1000 is implemented on a computer (say aserver computer) in remote communication with computer, say a user'smobile phone, or other computer in use for capturing one or more imagesof a background object, at least partially through a lens, as describedin further detail hereinabove.

Thus, optionally, all parts of the apparatus 1000 are implemented on theserver computer, as described in further detail hereinbelow.

Optionally, one or more of the apparatus 1000 parts may be implementedat least in part, on the mobile phone, tablet computer, or othercomputer in communication with the server computer, etc., as describedin further detail hereinbelow.

Each one of the parts denoted 120-130 in FIG. 1B, the image receiver,and the additional parts, may be implemented as software—say byprogramming the one or more computer processors to execute steps of themethods described in further detail hereinbelow, as hardware—say as anelectric circuit that implements at least a part of the methods, etc.,or any combination thereof.

The image receiver may receive the image, for example, over theinternet, an intranet network, a LAN (Local Area Network), a wirelessnetwork, another communication network or channel, or any combinationthereof, as known in the art.

Reference is now made to FIG. 2A, which is a flowchart illustrating afirst exemplary computer implemented method for self-administratedtesting of eyeglasses, according to an exemplary embodiment of thepresent invention.

The exemplary method for self-administrated testing of eyeglasses may beexecuted, for example, on one or more computers such as a mobile phone,a tablet or other computer, say a computer in use by a user who wishesto test a pair of eyeglasses (say eyeglasses or other eyeglasses), etc.or any combination thereof.

The exemplary method may be implemented by programming the computer, sayby downloading a computer application from a remote server, by uploadingcomputer executable instructions from a computer readable medium, etc.,as known in the art.

Thus, the method may include steps that are executed by a computerapplication such an iPhone® or an Android™ App, which may be downloadedto a mobile phone (say an Apple® iPhone or a Samsung® Galaxy smartphone) or table computer (say an Apple® iPad). The computer applicationmay use the mobile phone's camera and screen, for carrying out some ofthe steps of the exemplary method, as described in further detailhereinbelow.

In a first example, all method steps such as the steeps of capturing210, analyzing 220 and identifying 230, as described in further detailhereinbelow, are carried out on the computer, say on the user's mobilephone, tablet computer or laptop computer, by the computer application.

In a second example, the computer application rather communicates with aremote computer (say a remote server computer), for carrying out one ormore parts of the method steps. Thus, in the second example, at least apart of the below described steps of analyzing 220 and identifying 230,is carried out on the remote computer (say the server computer).

Optionally, in the second example, the step of capturing 210 is alsocarried out on the remote server computer, say by remotely controllingan image capture device (say a mobile phone camera) in use (say by theuser), for capturing 210 a background image 210 in one or more images,as described in further detail hereinbelow.

Thus, in the exemplary method, there is captured 210 one or more stillsor video images of a background object such as a human face, a patternpresented on a screen (say a computer screen, a screen or paper on whichthe pattern is projected from a slide projector, etc.), a patternprinted on a physical object (say on paper), etc. The pattern may bedefined in advance—say by a programmer or an operator of apparatus 1000.

Optionally, the background object is captured 210 in the image using animage capture device (say the user's mobile phone camera), say by theimage capturer 110 of apparatus 1000, as described in further detailhereinabove.

Optionally, the image capture device is a video camera installed on amobile phone in use by the user, and the image capturer 110 controls thecamera, say by using the camera to capture 210 images, by forwarding oneof the images for analysis 220, etc., as described in further detailhereinabove.

In other examples, the image capture device may be a camera installed ona tablet computer, on a laptop computer, or on another computer, asdescribed in further detail hereinabove.

At least a part of the background object is captured 210 through alens—say a lens of a pair of eyeglasses that belongs to a user of thecomputer (say mobile phone).

Optionally, one or more remaining parts (if any) of the backgroundobject are also captured 210 in the image, but rather directly—i.e. notthrough the lens, as described in further detail hereinbelow, and asillustrated, for example, in FIG. 9.

In a first example, a background object in a form of a predefinedpattern is presented on a digital screen (say an LCD screen, as known inthe art).

In the first example, simultaneously to presentation of the predefinedpattern on the digital screen, a user hold his eyeglasses opposite thescreen, or rather places the eyeglasses on a surface (say a desktop)opposite the screen.

Then, there is captured 210 the predefined pattern in an image, suchthat one part of the pattern is captured through one of the eyeglasses'lenses. The remaining part of the pattern extends out of the area behindthe lens, and is thus captured directly—i.e. not thorough the lens.

Then, one or more of the captured 210 images are analyzed 220, say bythe image analyzer 120 of apparatus 1000, as described in further detailhereinabove.

Optionally, the analyzing 220 includes analyzing 220 the image's colorcharacteristics of the image.

For example, the image may be a digital color image based on the RGBcolor model, and the analyzing 220 may include a comparison based on RGBcolor channel intensity values measured over the background object (saythe predefined pattern) as captured 210 in the image, as described infurther detail hereinbelow.

The digital color image may be made of many pixels, and each one of thepixels may be made of a combination of three primary colors (Red, Greenand Blue).

A channel in this context is the equivalent of a grayscale image (or apart of such an image) of the same size as the digital color image (orthe part), made of one of the primary colors (rather than grey). Thusthe channel may also be perceived as a monochromatic image in red, greenand blue.

Each one of the monochromatic images (i.e. channels) is made of a samenumber of pixels as the color image or part, which in the case of themonochromatic image, are pixels in a same primary color, which aresometimes referred to as sub-pixels. Similarly to a grayscale imagepixel, each of the monochromatic image's pixels is characterized by anintensity specific to that pixel.

Thus, each RGB color image (or a part of such an image) may becharacterized by a combination of three channels (Red, Green, and Blue).

In a first example, the analysis 220 includes a comparison made betweenone or more same color channel intensity values (say one or more mean orsum intensity values, each of which values is calculated over all pixelsof a respective one of one or more of the channels) of two parts of thebackground object as captured 210 in the image.

In the first example, the two parts are supposed to have a same color.For example, the background object may be a predefined pattern and thetwo parts may have a same color in the pattern as predefined (say by auser of apparatus 1000), say two parts of one of the slices of theradial pattern illustrated in FIG. 5 hereinbelow.

However, in the image, one of the parts is captured 210 through thelens, whereas a remaining part is captured 210 directly—i.e. not throughthe lens, as described in further detail hereinbelow. Consequently, thecolor of the part captured 210 through the lens may differ from thecolor of the part captured 210 directly.

In the example, one comparison is made between blue channel intensityvalues of the two parts and another comparison is made between redchannel intensity values of the two parts.

The comparison is thus to assess a per-channel (i.e. per primary color)difference between the part of the background object captured 210through the lens and the part of the background object pattern that israther capture 210 directly (i.e. not through the lens).

In a second example, the analysis 220 includes a comparison made betweenabsolute differences or rations, calculated between different colorchannel intensity values (say a mean or sum intensity value calculatedover all pixels of each respective one of one or more of the channels).The absolute differences or ratios are calculated for two differentparts of the background object as captured 210 in the image—say for apart captured 210 through the lens, and for a part captured 120 directly(i.e. not through the lens).

The comparison is thus used to assess how the two parts differ in as faras the ratio or absolute difference between different primary colors isconcerned.

In a third example, the analysis 220 includes a comparison made betweenone or more color channel intensity values (say mean or sum intensityvalues, each one of which values is calculated over all pixels of arespective one of one or more of the channels) and a reference valuepredefined per the respective channel.

The one or more color channel intensity values may be calculated for thewhole background object as captured 210 in the image, or rather per eachone of one or more different parts of the background object as captured210 in the image. Similarly and respectively, the one or more referencevalues may be defined for the whole background object or rather per eachone of the one or more parts of the background object.

The comparison is thus used to assess a per-channel (i.e. per primarycolor) deviation from the predefined reference values.

In a fourth example, the analysis 220 includes calculating one or morecolor channel intensity values (say mean or sum intensity values, eachone of which values is calculated over all pixels of a respective one ofone or more of the channels). Again, the one or more color channelintensity values may be calculated for the whole background object ascaptured 210 in the image, or rather per each one of one or moredifferent parts of the background object as captured 210 in the image.

In the fourth example, the analysis 220 further includes calculatingabsolute differences or ratios between the color channel intensityvalues. Then, the differences or ratios are compared to reference valuespredefined for the whole background object or rather per each one of theone or more parts of the background object.

The comparison is thus used to assess the deviation of the wholebackground object as captured 210 in the image, or rather of one or morespecific parts of the captured 210 background object from a predefinedratio or absolute difference between primary colors.

Optionally, in the fourth example, there are further compared thedeviations from the predefined ratios or absolute differences, between apart of the background object that is captured 210 through the lens anda part of the background object that is captured 210 directly (i.e. notthrough the lens).

Optionally, the background object is a predefined pattern and the methodfurther includes a preliminary step in which there is calculated the oneor more reference values, say by the reference value calculator ofapparatus 1000, as described in further detail hereinabove.

Optionally, in the preliminary step, the one or more reference valuesare calculated per each specific one of the predefined pattern's partsbased on the specific part's coloring in the predefined pattern, asdescribed in further detail hereinbelow.

The calculation may be based, for example, on an averaging or summingover intensity values expected for that part upon presentation on ascreen of a specific model, in light of the screen's technicalcharacteristics and the color selected for that part.

Optionally, the color is selected for the part based on the screen'stechnical characteristics which may be input say by an operator orprogrammer of apparatus 1000, as described in further detailhereinabove.

Additionally or alternatively, the preliminary step includes allowingone or more of the reference values to be defined by an operator orprogrammer of the apparatus 1000, as described in further detailhereinabove.

Based on the image analyzed 220, say based on the analysis 220 of thecolor characteristics of the image, there may be identified 230 aproperty of the lens, say by the property identifier 130, as describedin further detail hereinbelow.

Optionally, the property is identified 230 based on the color channelintensity values, say using one or more of the comparisons illustratedusing the examples provided hereinabove, as described in further detailhereinabove.

Additionally or alternatively, the analyzes 220 may include using theRGB color channel intensity values measured over the whole area of thebackground object as captured 210 in the image, or rather over specificparts of the captured 210 background object, to calculate atransmittance spectrum (say a spectral graph, as known in the art).

For example, the RGB color channel intensity values may be used tocalculate a VIS (Visual Light) Spectrum in the 400-700 NM wave lengthrange, using color estimation methods, as described in further detailhereinbelow.

Consequently, the calculated spectrum may be compared to a predefinedreference spectrum or to a spectrum calculated over a part of thebackground object, which part is supposed to bear the same color.

Thus, in one example, the background object is a predefined patternpresented on a computer screen standing behind the eyeglasses. In theexample, there is calculated a reference spectrum for the wholepredefined pattern or rather per each specific one of one or more partsof the pattern.

For example, the reference spectrum may be calculated based on coloringin the predefined pattern and on characteristics of a screen in use forpresenting the pattern, say using one of the methods described infurther detail hereinbelow.

Optionally, the analyzing 120 includes a comparison made between a pairof spectrums. One of the spectrums is a spectrum calculated based on RGBcolor channel intensity values measured over a part of the backgroundobject that is captured 210 through the lens. A second one of thespectrums is a spectrum calculated based on RGB color channel intensityvalues measured over a part that is captured 210 directly (i.e. notthrough the lens). Consequently, one or more properties of the lens maybe identified 230, base on the comparison made between the twospectrums.

In the example, in a preliminary step which follows the design of apredefined pattern intended for use as the background object, a lightspectrometer is used to measure light intensities over a visual lightwavelength range of say 400-700 NM, for the pattern when presented on adigital screen. In the example, the pattern is similar to the pattern5000 illustrated in FIG. 5, and each slice 501-512 bears a differentcolor.

Based on the light intensities measured using the light spectrometer,there is calculated a specific reference spectrum (say a curve depictinglight intensity as a function of wavelength) for each one of the twelveslices 501-512, each of which slices is colored uniformly, though with acolor different from the others' 501-512.

In the example, as a part of the analysis 220 step, a triad of RGBchannel intensity values of each slice's 501-512 part captured 210 (sayby a digital camera) in the image directly (i.e. not through the lens)and the reference spectrum calculated for that slice in the preliminarystep, are construed as matching.

Based on the twelve pair of matched triads and reference spectrums,there is calculated a spectrum fitting function which matches a spectrumfor any possible triad of RGB channel intensity values. The spectrumfitting function may be calculated using Polynomial Curve Fitting basedon Least Square Fitting, Wiener Estimation, etc., as known on the art.

Then, using the spectrum fitting function, there is matched a secondspectrum for each slice 501-512, based on a triad of RGB channelintensity values of the slice's 501-512 part captured 210 in the image,through the lens.

Consequently, for each slice 501-512 there is compared the secondspectrum matched to the slice 501-512 and the reference spectrumcalculated for the slice 501-512 in the preliminary stage, foridentifying 230 a property of the lens, say using the propertyidentifier 130, as described in further detail hereinabove.

Optionally, the analysis 220 further includes identifying a predefineddeformation along a segment within the background object—say thepredefined pattern.

In one example, the segment is a virtual linear segment which extendsbetween two points inside a blank part of the predefined pattern ascaptured 210 in the image.

The two points may be selected arbitrarily or rather so as to examineone or more specific regions of interest—say an area in the middle ofthe background object as captured 210, an area around the center of thelens, a part partially captured 210 through the lens and partiallycaptured 210 not through the lens, etc. as described in further detailhereinbelow.

The deformation may include, but is not limit to: an unexpecteddiscontinuity of the segment, a change in curvature of the segment (sayan unexpected sinusoidal form or a high frequency of amplitude changes,when expected to be straight), etc., as described in further detailhereinbelow.

Optionally, as a part of the analysis 220, there is further identified adifference in sharpness between a part of the background object captured210 through the lens and a part of the background object captured 210directly (i.e. not through the lens).

The sharpness of may be calculated, for example, using Absolute CentralMoments, Diagonal Laplacians, etc., as known in the art.

Subsequently, based on the identified deformation, sharpness difference,or both, there is identified 230 a property of the lens, as described infurther detail hereinbelow.

Optionally, the analyzing 220 includes using one or more functions basedon ne or more of the above described comparisons, identifying of thedeformations, and identifying of the sharpness difference.

In one example, each one of the functions yields an indicator usable foridentifying a different, respective property of the lens through whichat least a part of the background object is captured 210 in the image.

Optionally, the identified 230 property is one of having a predefinedcoating, a predefined filter, or of having both the coating and thefilter, say by the property identifier 130, as described in furtherdetail hereinabove.

Optionally, the identified 230 property is one of having a High EnergyVisible (HEV) light protective coating, a HEV light protective filter,or both the HEV light protective coating and the HEV light protectivefilter.

Optionally, the property of having the HEV protective coating, filter,or both, is identified 230 based on a significantly lower mean of pixelintensity values in the RGB Blue channel in parts of the backgroundobject captured through the lens when compared to parts captureddirectly (i.e. not through the lens).

Thus, in one example, the lens property of having the HEV protectivecoating, filter, or both, is identified 230 based on a comparison madebetween parts of the background object that are known to have the samecolor (say parts of a background object, the whole of which object ispainted with a same color).

In the example, the property is identified 230 when the mean of pixelintensity values in the RGB Blue channel in the background object'sparts captured 210 through the lens is at least 10% lower than in thebackground object's parts captured 210 directly (i.e. not through thelens).

Optionally, the analysis 220 includes calculating the means over allpixels in an area of the image occupied by the background object (say apredefined pattern presented on a screen or on paper), or ratherspecifically per each one of one or more parts of the area occupied bythe background object, over all pixels in that part, as described infurther detail hereinbelow.

In one example, the background object is a predefined pattern that iswhite and black, and the mean intensity values are calculated for thewhole area occupied by the predefined pattern, in the RGB Red and Bluechannels. In the example, the mean intensity value for each one of thetwo channels is calculated and rounded to the nearest integer.

In the example, when the ratio of the rounded mean intensity value ofBlue channel to the rounded mean intensity value of the Red channel isbelow 0.90, there is identified 230 the property of having a HEVcoating, HEV filter, or both.

In one example, the image capture device is an eight bit digital camera,as known in the art.

In the example, when the mean intensity value calculated for the RGBBlue channel is lower than 200 and the mean intensity value calculatedfor the RGB Red channel is higher than 240, there is identified 230 theproperty of having a HEV coating, a HEV filter, or both.

Optionally, the identified 230 property is one of an effectiveAnti-Reflection (AR) coating or filter.

Optionally, there is identified 230 that the lens has an effectiveAntiReflection (AR) coating or filter, based on a reduction of less than3% in the mean intensity value of each one of the RGB channels whencompared to predefined reference values, as described in further detailhereinbelow.

Optionally, there is identified 230 that the lens has the effective ARcoating or filter, based on a less than 3% reduction in the meanintensity value of each one of the RGB channels in a part of thebackground object that is captured 210 in the image, through the lens,when compared to a part that is known to have a same color, but iscaptured 210 directly (i.e. not through the lens).

The mean intensity values may be calculated per the whole area of theimage occupied by the background object (say predefined pattern), orrather specifically per each one of one or more parts of the image areaoccupied by the background object, as described in further detailhereinbelow.

Thus, in one example, when the image capture device is an eight bitdigital camera and the distance between the image capture device and thelens is small enough (say less than 40 centimeters), the device'smaximal intensity value of 255 (in the 0-255 range possible with eightbits) is likely to be the maximal intensity actually measured.

Consequently, in the example, when all or a predefined one or more ofthe RGB channel mean intensity values is at least 248 (i.e. higher than97% of 255), there is identified 230 the property of having an effectiveAR coating or filter.

However, with a longer distance, the maximal intensity actually measuredmay be lower. In one example, the maximal intensity actually measured is200, and consequently, in the example, when all or a predefined one ormore of the RGB channel mean intensity values is higher than say 194(i.e. 97% of 200), there is identified 230 the property of having aneffective AR coating or filter.

Optionally, the identified 230 property is one of a degree of darknessof the lens.

In one example, there is identified 230 that the lens are too dark basedon a ratio calculated in the analysis 220 step between two overall meanintensity values.

The first overall mean intensity value is calculated over all RGBchannels, for all parts of the background object that are captured 210through the lens, and the second overall mean intensity value iscalculated over all RGB channels, for all parts of the background objectthat are captured 210 directly (i.e. not through the lens).

Optionally, the property identifier 230 (say by the property identifier130) is a one of a roughly estimated color of the lens.

In one example, the identifying 230 involves a rough estimation of thecolor of the lens, based on a comparison made between two triads of RGBmean intensity values.

The first triad consists of RGB mean intensity values calculated per RGBchannel, over a part of a white area of the background object ascaptured 210 through the lens. The second triad consists of RGB meanintensity values calculated per RGB channel, over a part of the whitearea of the background object as captured 210 directly (i.e. not throughthe lens).

Thus, in one example, the mean intensity value calculated for the greenchannel over the part captured 210 through the lens, as a part theanalysis 220, is at least 95% of the mean intensity value calculated forthe green channel over the part captured 210 directly. However in theexample, the calculated red and blue mean intensity values differ moresignificantly between the two parts. Consequently, there is identified230 the property of the lens being roughly, green.

Additionally or alternatively, the background area is rather non-white(say an area that is uniformly colored green, blue, purple, etc.).

Optionally, based on the identified 230 property of the roughlyestimated color, there may be further identified 230 a property ofdiscoloration or yellowness of the lens, say when there is known inadvance (say through input from an operator of apparatus 1000) that thelens is supposed to be colorless.

Optionally, the identified 230 pertains to a driving compatibility ofthe lens.

The identifying 230 of the property which pertains to the drivingcompatibility of the lens may be based on one or more requirements, asspecified, for example, in the ISO (International Organization forStandardization) 8980-3 2013 standard.

A key concern when identifying a driving compatibility of a lens of apair of eyeglasses is a driver's ability to tell one color from theother, when viewed through the lens. For example, when light in acertain primary color is not transmitted fully through the lens, theremay be distorted the color as captured 210 through the lens.Consequently, the driver may be unable to tell one traffic light fromanother.

Thus, in one example made with reference being diverted to FIG. 5, theidentifying 230 of the driving compatibility property (say by theproperty identifier 130) is based on the radial pattern 5000 illustratedusing FIG. 5 hereinbelow.

In the example, each one of the pattern's 5000 parts (also referred tohereinbelow as slices) 501-512 arranged around the pattern's 5000central white area 500 is designed with a different color, as describedin further detail hereinbelow.

Further in the example, when designing the pattern 5000, it is made surethat for each one of the parts 501-512, a mean intensity valuecalculated for at least one of the RGB channels would differ in at least40% from a mean intensity value calculated for each one of the otherparts 501-512, for that channel.

In the example, as captured 210 in the image, the predefined pattern's5000 white central area 500 aligns under the center of the lens, suchthat a part of each slice 501-512, which part is proximal with respectto the central area 500, is captured 210 through the lens. However, theremaining part, which part is distal with respect to the center of thelens, is captured 210 directly (i.e. not through the lens).

That is to say that in the example, the lens is smaller than the patternas being presented, and consequently, when the white central area 500aligns under the center of the lens, each one of the slices 501-512arranged around the white central area 500 is captured 210 partiallythrough the lens and partially directly, from around the lens.

Optionally, the mean intensity values are re-calculated over each part's501-512 part that is distal with respect to the center 500 of thepattern as captured 210 in the image, which distal part is captured 210directly (i.e. not through the lens). Then, the difference of at least40% among the mean intensity values calculated for the different parts501-512, for that channel is confirmed.

In the example, as a part of the analysis 220 of the image, for each andevery one of the parts 501-512 as captured 210 through the lens, a meanintensity value is calculated per each one or more of the RGB channels,as described in further detail hereinabove.

Then, if for at least one of channels, the mean intensity valuecalculated for that part, differs in at least 20% from a mean intensityvalue calculated for each one of the other parts 501-512, for thatchannel, there is identified 230 that the lens fits driving. The meansintensity values are calculated over the part's part that is proximalwith respect to the center 500 of the pattern, which proximal part iscaptured through the lens, as illustrated in FIG. 5.

Optionally, the analysis 220 further includes identifying a predefineddeformation along a virtual segment within the background object—say thepredefined pattern, say along a linear segment which extends between twopoints inside a blank part of the predefined pattern, as described infurther detail hereinabove.

The two points may be selected arbitrarily or rather so as to examineone or more specific regions of interest and for identifying adifference between the regions. For example, a region of interest (ROI)may be an area in the middle of the background object as captured, anarea around the center of the lens, a part partially captured throughthe lens and partially captured not though the lens, etc. as describedin further detail hereinabove.

Optionally, the analysis 220 further includes identifying a predefineddeformation along a real segment within the background object—say a lineforming a part of the predefined pattern, as described in further detailhereinabove.

The predefined deformation may be identified, for example, using cannyedge detection. Hough transform methods, a measurement of the lengths ofcontinuous lines or circles, etc., or any combination thereof, as knownin the art.

Optionally, as a part of the analysis 220, there is further identified adifference in sharpness between a part of the background object captured210 through the lens and a part of the background object captured 210directly (i.e. not through the lens), as described in further detailhereinabove.

Based on the identified deformation, sharpness difference, or both,there may be identified 230 one or more properties of the lens (say ofhaving a scratch, crack, or peeling), as described in further detailhereinbelow.

Thus, in a first example, an identified deformation includes anunexpected discontinuity of the segment, and there is further identifieda blur (i.e. reduction of sharpness) in the segment's part captured 210through the lens when compared to the segment's part captured 210directly (i.e. not through the lens).

More specifically, in the example, the discontinuity of the segment isfound only in a part of segment that is captured 210 through the lens,whereas the part of the segment captured 210 directly seems continuous.

In the example, the deformation is measured through FFT (Fast FourierTransform) based analysis 220 of grayscale or specific RGB channel'sintensity values along the segment, and the analysis 220 shows highamplitude at high frequency along a part of the segment that is captured210 through the lens. The FFT analysis thus shows high changes over arelatively small area (relative to the segment's width) in the partcaptured 210 through the lens.

In the example, a high amplitude is defined as a one that is say, tentimes higher than the amplitude resultant upon that FFT analysis 220when carried out on a part of the segment that is captured 210 directly(i.e. not through the lens).

In the first example, based on the high amplitude at high frequency andthe blur, there may be identified 230 a property—say a presence of oneor more scratches on the lens.

In a second example, an identified deformation may include a palingeffect along a part of the segment (say the thin black line) capturedthrough 210 the lens, though the segment may still maintain itscontinuity along that part. Further, in the second example, there isalso identified a blur (i.e. a reduction of sharpness) in the segment'spart captured 210 through the lens when compared to the segment's partcaptured 210 directly (i.e. not through the lens), as described infurther detail hereinabove.

More specifically, in the second example, the paling effect is foundonly in the part of segment that is captured 210 through the lens.

In the example, the deformation is measured through FFT (Fast FourierTransform) based analysis 220 of grayscale or specific RGB channel'sintensity values along the segment, and the analysis 220 shows highamplitude at low frequency along a part of the segment that is captured210 through the lens. The FFT analysis thus shows low changes over arelatively wide area (relative to the segment's width) in the partcaptured 210 through the lens.

In the example, a high amplitude is defined as a one that is say, tentimes higher than the amplitude resultant upon that FFT analysis 220 ascarried out on a part of the segment that is captured 210 directly (i.e.not through the lens).

In the second example, based on the high amplitude at low frequency andthe blur, there may be identified 230 a property—say a presence ofpeeling on the lens.

In a third example, an identified deformation includes an unexpecteddiscontinuity of a segment. the discontinuity of the third example is aone characterized by several white cuts along the segment.

In the third example, based on the identified deformation, there may beidentified 230 a property—say a presence of cracks on the lens.

In a fourth example, an identified deformation includes an unexpectedsinusoidal or other waveform appearance of a segment supposed bestraight. Then, based on the waveform appearance, there is identified230 a property of an inconsistency of the lens.

Optionally, there is further identified 230 a property that has to dowith opacity of the lens or a general quality of the lens, say by usingone or more of the properties mentioned hereinabove to roughly estimatethe opacity of the lens.

Thus, in one example, the property identifier 130 may estimate theopacity based on a count of the number of scratches or cracksidentified, on an assessment of the percentage of lens area that iscovered by scratches, etc.

In the example, for estimating the opacity, the property identifier 130may use a reference table predefined by an operator or programmer, whichtable gives a level of opacity per a number of scratches and cracks, peran estimated percentage of the lens area that is covered by scratches,etc.

Optionally, when captured 210, the predefined pattern is presented on ascreen (say a computer screen, a mobile phone screen, etc.), say by thepattern projector, as described in further detail hereinabove.

Optionally, when captured 210, the predefined pattern is ratherprojected onto a surface such as a paper sheet, etc., as known in theart. For example, the predefined pattern may be projected from a lightsource such as a slide projector, a computer screen, a screen of amobile phone, a flashlight, an indoor or other light (say through slidewhich bears the pattern), etc.

Optionally, the projection of the predefined pattern is carried out by afirst computer (say the user's computer), by a remote computer incommunication with the first computer or with the light sources, orboth, as described in further detail hereinabove.

In one example, in a preliminary step, for predefined pattern (i.e. thebackground object of the example) is presented on a screen of a laptopcomputer.

Optionally, in the example, a user places a pair of eyeglasses on asurface such as a desktop, such that at least a part of the presentedpattern can be captured through 10 a lens of the eyeglasses,simultaneously to presentation of the pattern on the screen.

Then, the predefined pattern (i.e. the background object of the instantexample) is captured 210 in an image, say using a camera of the user'smobile phone (say smart phone), as described in further detailhereinbelow.

The capturing 210 of the background object in the image may be triggeredby a manual operation—say a user's clicking on a button presented in aGUI, on the phone's screen, or rather automatically—say upon alignmentof the background object in a predefined way, as described in furtherdetail hereinbelow.

Optionally, on the background object (say the predefined pattern), thereappears a marking usable by the user for aligning the pair of eyeglassesand the image capture device in a predefined orientation with respect toeach other, prior to the capturing 210, as illustrated, for example, inFIG. 5 and FIG. 6.

Optionally, the background object (say the predefined pattern has atexture that has a directional aspect usable by the user for aligningthe pair of eyeglasses and the image capture device in a predefinedorientation with respect to each other, prior to the capturing 210, asillustrated, for example, in FIG. 7.

Optionally, the method further includes a preliminary step in whichcolor selection for one or more parts of the background object (say apredefined pattern) is optimized according to technical characteristicsof the image capture device (say a digital camera), of a device used forpreventing the pattern (say a digital screen), or of both.

The light emission difference among different digital screens, is nowexplained with reference being made to FIG. 3, which is a simplifieddiagram schematically illustrating an exemplary graph depicting anemission spectrum of one exemplary digital screen.

Digital screens such as Liquid Crystal Display (LCD) or LED(Light-Emitting Diode) computer and smart phone screens may differ intheir emission spectra. Those screens are often characterized astri-chromatic light sources that emit light using three basic types oftiny light sources also referred to as sub-pixels—Red, Green, and Blue.

For each digital screen, a graph depicting intensity of the lightemitted from the digital screen, usually peaks at a first wavelengththat is in the range of red light (˜570-700 nm), as a second wavelengththat is in the range of green light (˜500-580 nm), and at a thirdwavelength that is in the range of blue light (˜430-490 nm).

However, for each different screen model, the graph usually peaks at adifferent first (i.e. red light) wavelength, a different second (i.e.green light) wavelength and a different third (i.e. blue light)wavelength.

Thus, for one exemplary digital screen, the graph 3000 has a first peak310 at a wavelength within the ˜570-700 nm range of red light, a secondpeak 320 at a wavelength within the ˜500-580 nm range of green light,and a third peak 330 at a wavelength within the ˜430-490 nm range ofblue light.

The light sensitivity difference among different digital cameras is nowexplained with reference being made to FIG. 4, which is a simplifieddiagram schematically illustrating an exemplary set of three graphs,depicting sensitivity of one exemplary digital camera to three primarycolors.

A stills camera or a video camera usually includes many small sensorssuch as CMOS (Complementary Metal Oxide Semiconductor) sensors or CCD(Charge Coupled Device) sensors.

Those small sensors are used for capturing the image, and have differentsensitivity for each one of the Red, Green, and Blue primary colors,respectively.

However, the sensitivity of the sensors to each one of the primarycolors also varies among different models of digital cameras.

Consequently, for a different camera model (or other image capturedevice model), the peak of sensitivity for each one of the three typesis usually different.

Thus, for one camera model, a graph 4100 depicting the sensitivity ofred light sensors picks 410 at a first wavelength, a graph 4200depicting the sensitivity of green light sensors picks 420 at a secondwavelength, and a graph 4300 depicting the sensitivity of blue lightsensors peaks 430 at a third wavelength.

However, for a different camera model, each one of the three graphs4100-4300 may be different and pick at a different wavelength.

An exemplary optimization of color selection for different parts of abackground object (say a predefined pattern), according to technicalcharacteristics of an image capture device—say a digital camera, of adevice used for presenting the pattern—say a digital screen, or both, isnow explained with reference being made to FIG. 5.

FIG. 5 is a simplified diagram schematically illustrating an exemplaryradial predefined pattern, according to an exemplary embodiment of thepresent invention.

One exemplary background object is a radial pattern 5000 which may bepresented on a computer screen, printed on paper, projected onto ascreen surface, etc., as known in the art.

The radial pattern 5000 is a round pattern predefined, say by aprogrammer or operator of apparatus 1000.

As illustrated using FIG. 5, the predefined radial pattern 5000 has twoor more parts 501-512 in a form of slices 501-512 arranged around acenter of the pattern 5000, and more specifically, around a roundcentral area 500 of the pattern 5000.

Optionally, the pattern 5000 is a colored pattern. Alternatively, thepattern is rather non-colored, or rather a pattern that includes one ormore colored parts and one or more parts that are not colored (say oneor more grayscale or white parts).

Optionally, the pattern 5000 is radially symmetric—i.e. symmetric withrespect to the center of the pattern 5000, such that parts 501-512positioned opposite each one, on opposite sides of the pattern's 5000center have the same color and texture. Consequently, the radiallysymmetric pattern may be captured 210 in an image, through both lensesof a pair of eyeglasses, such that a pattern part captured 210 throughone lens is symmetric to a pattern part captured 210 through anotherlens, and a comparison between the two parts reveals a differencebetween the lenses.

Optionally, the radially symmetric pattern may be captured 210 in animage, such that one of the pattern's parts 501-512, which part iscolored uniformly, is captured 210 in the image. One part of theuniformly colored part is captured 210 through one of the lenses, andthe remaining part of the uniformly colored part is captured 210directly (i.e. not through a lens). Consequently, a comparison madebetween the two parts of the uniformly colored part is captured 210 inthe image, may help identify 230 a property of the lens, as described infurther detail hereinbelow.

In one example, the predefined pattern 5000 is a colored patterndesigned with a color selection that corresponds to the wavelength atwhich light emitted from a digital screen used to present the patternpeaks, as described in further detail hereinabove.

Thus, in the example, some of the parts 501-512 of the exemplary radialpattern 5000 may be designed with colors that match the maximal huevalues for the basic colors (Blue, Green and Red) possible with thedigital screen in use.

Optionally, some of the parts (i.e. slices) 501-512 may be based oncolor combinations that are halfway between those hue values, say halftones, as known in the art.

Thus, each slice 501-512 in the predefined radial pattern 5000 isdesigned with a respective, predefined color, and a respective,predefined position within the pattern.

Alternatively or additionally, the color selection made with respect tothe slices 501-512 of the exemplary radial pattern 5000, may similarlytake into consideration the wavelengths at which the sensitivity graphsof the image capture device (say a digital camera) peak, as described infurther detail hereinabove.

The exemplary radial pattern 5000 further includes a marking 550 whichextends from the central area 500 of the pattern 5000 on which a crossforming a first end of the mark is shown in FIG. 5, to the right.

The marking 550 may be used for automatically identifying theorientation of the pattern 5000 when captured 210 at least partiallythrough the lens, guide the user in aligning the pattern 5000 in aspecific orientation, etc., as described in further detail hereinbelow.

Further, the marking 550 reveals the location of each specific one ofthe parts 501-512, since the order of placement of the specific part inthe pattern 5000, with respect to the marking 550 is known.

Consequently, the marking 550 allows an analysis 220 of the pattern 5000when captured 210 in the image, say by comparing a color of each one ofthe parts 501-512 as captured 210 in the image and the color selectionfor that part (say by comparing hue values measured over the part andhue values calculated based on the color selection for the part).Similarly, the marking 550 allows an analysis 220 of the pattern 5000 ascaptured 210 in the image, by comparing between each part's 501-512 partcaptured 210 through the lens and the part's 501-512 part captureddirectly, taking into consideration the color that the part is designedwith, as described in further detail hereinabove.

With reference being made back to FIG. 2A, it is noted that thebackground object (say the predefined pattern—whether that of FIG. 5that of FIG. 6, or say other predefined pattern), may be designed invarious ways.

For example, the predefined pattern may be colored, non-colored (saywhite or grayscale), or rather a one that has one or more colored partsand one or more noncolored (say grayscale or white) parts, as describedin further detail hereinabove

Optionally, the predefined pattern may be blank, non-blank (say a onewith graphical or textual content), or rather a one that has one or moreblank parts and one or more non-blank parts.

Optionally, the predefined pattern includes two or more parts ofdifferent color, color level (say gray level) or texture, and each parthas a respective, predefined color, color level or texture, and arespective, predefined position within the pattern.

Consequently, the analysis 220 of the captured 210 image may be based onthe color, color level or texture of at least one of the parts, and onthe part's position within the background object.

In one example, the background object is a radial predefined patternthat has two or more parts of different color, arranged around a centerof the predefined pattern, as describe in further detail hereinabove,and as illustrated, for example, in FIG. 5.

In the example, each part has respective, predefined color and order ofplacement around the center, and the analysis 220 of the captured 210images is based on the respective predefined order of placement of atleast one of the parts, and on the part's color, say through thecomparisons described in further detail hereinabove.

Optionally, the background object includes one or more predefinedmarkings.

In one example, when the background object aligns in a preferredorientation, the marking aligns in a predefined way. For example, whenthe exemplary pattern 5000 illustrated in FIG. 5 aligns in a preferredorientation, the marking 550 extends rightward from the center of theradial pattern 5000, as illustrated in FIG. 5.

In other cases, the markings, may include, for example a number, anarrow, a cross, a line, words, letters, etc., as described in furtherdetail hereinbelow.

Thus, in a second example, illustrated with reference being made to FIG.6 which is a simplified diagram schematically illustrating a firstexemplary planar predefined pattern, according to an exemplaryembodiment of the present invention, the marking includes a cross mark610 that is adjacent to one of pattern's 6000 angles.

Returning to FIG. 2A, it is noted that one or more of the markings maybe used by a user to align the pair of eyeglasses and the image capturedevice used to capture 210 the background object in the image, such thatthe captured 210 object aligns in a predefined way, as described infurther detail hereinbelow.

Additionally or alternatively, the markings may be automaticallyidentified and located in the captured 210 image, say using theflood-fill algorithm, as known in the art. Once identified and located,the located markings may be used to automatically identify anorientation of the background object, identify the object's center,etc., as described in further detail hereinbelow.

Optionally, the background object is a pattern predefined with a texturemade of stripes, geometric shapes, etc., and the background object'sorientation may rather be identified based on a directional aspect ofthe texture of the predefined pattern.

Thus, in one example, illustrated with reference being made to FIG. 7which is a simplified diagram schematically illustrating a secondexemplary planar predefined pattern 7000, according to an exemplaryembodiment of the present invention, the orientation of the pattern 7000may be identified based on the orientation of the stripes 710.

Further, the user himself may use the texture's directional aspect, foraligning the eyeglasses in a predefined, preferred orientation withrespect to the image capture device, say into a position in which thepattern 7000 is captured 210 partially through the lens and partiallydirectly, with the stripes 710 aligned vertically.

Returning to FIG. 2A, it is noted that optionally, an identifying of thebackground object's orientation, center, or both, is used for analyzing220 the captured 210 image, for guiding the user in aligning theeyeglasses and image capture device (say by moving his mobile phone) ina specific orientation with respect to each other, etc., as described infurther detail hereinbelow.

Thus, optionally, the method further includes locating a predefinedmarking which appears on the background object (say predefined pattern)as captured 210 in the image, and when identifying alignment of thepredefined marking in a predefined orientation, automatically initiatingthe analyzing 220.

Optionally, the method further includes locating a predefined part ofthe background object, say the round area 550 around the radialpattern's 5000 center as illustrated in FIG. 5, and automaticallyinitiating the analyzing 220 upon a predefined alignment of the part—saya positioning over a predefined area of the lens, etc.

Reference being now made to FIG. 8, which is a simplified diagramschematically illustrating a second exemplary radial predefined pattern,according to an exemplary embodiment of the present invention.

In one example, the analysis 220 is initiated upon identified alignmentof a black and white pattern 8000 as captured 210 in the image in apreferred orientation as predefined, say by an operator or programmer ofapparatus 1000.

In the example, the predefined orientation is a one in which thepattern's 8000 center as captured 210 is within a predefined distancefrom the center of the lens, as described in further detail hereinbelow.

Optionally, only upon identifying the alignment in the predefined,preferred orientation is the analyzing 220 initiated.

Reference is now made to FIG. 9 which is a simplified diagramschematically illustrating an exemplary scenario of testing ofeyeglasses using a background object, according to an exemplaryembodiment of the present invention.

In one example, as a user moves a mobile phone over a pair of eyeglasses910 placed on a surface (say a desktop), in a position opposite acomputer screen 921.

As the user moves the mobile phone over the eyeglasses 910, a predefinedpattern 922 (which is the background object of the example) is presentedon the computer screen 921, and the apparatus 1000 (say the iPhone™ Appusing the mobile phone's camera) continuously captures 210 images of thepattern 922.

Then, the predefined pattern 922 is captured 210 in one of the images,partially through one of the eyeglasses' 910 lenses.

The capturing 210 of the predefined pattern 922 is triggered upon theuser's pushing a certain button on the mobile phone, or ratherautomatically upon alignment of the phone's camera and the eyeglasses910 in a predefined orientation with respect to each other, as describedin further detail hereinbelow.

As captured 210 in the image, the lens covers only a part of thepredefined pattern (i.e. background object of the example) 922.

Consequently, only a part of the predefined pattern 922 is captured 210through the lens, and the remaining part of the predefined pattern 922is captured 210 directly—i.e. not through the lens, as described infurther detail hereinbelow.

Then, the image is analyzed 220, say through one or more comparisonsmade between the predefined pattern's 922 part that is captured 210 inthe image through the lens and the predefined pattern's 922 part that isalso captured 210 in the image, but not through the lens.

Finally, based on the analysis 220 of the image, there may be identified230 a property of the lens—say the presence of a feature such as afilter or a layer for protection against UV (Ultraviolet) light or HEV(High-Energy Visible) light, a Driving Compatibility, or the presence ofa defects such as scratches, peeling, cracks, etc.

Further, in the example, the orientation of a predefined marking whichappears on the pattern 922 as captured 210 through the lens reveals thepattern's 922 orientation. When the marking aligns in a predefinedorientation, there is identified the pattern's 922 alignment in apredefined, preferred orientation.

For example, the pattern may be similar to the pattern 5000 illustratedin FIG. 5, and the orientation of the marking 550 may be a one in whichthe marking 550 extends horizontally from the center of the pattern 500to the right as illustrated in FIG. 5.

Thus, in the example, when there is determined that the marking 550aligns in the orientation illustrated in FIG. 5, say using the known inthe art flood-fill algorithm, there is determined that the pattern 922as captured 210 in the image, aligns in the predefined, preferredorientation.

In the example, based on the pattern's alignment is the preferredorientation, there is further determined that the pattern 922 and imagecapture device used to capture the pattern in the image (say mobilephone camera), align in predefined orientation with respect to eachother, as described in further detail hereinbelow.

Based on determining that the pattern 922 and image capture device usedto capture the pattern in the image align in predefined orientation withrespect to each other, the last image captured 210 by the phone's videocamera is forwarded for the analysis 220. The forwarding initiates theanalysis step 220 as described in further detail hereinabove.

Returning to FIG. 2A, it is noted that the marking, the part known tomark the background object's (say predefined pattern's) center, or both,may be located in the image using image processing or machine learningmethods, as known in the art.

The image processing methods may include, but are not limited to:Pattern Matching, Dangman Integro-Differential Operators, HoughTransformation for circle detection, etc., as known in the art.

Optionally, the identifying of the alignment of the marking whichappears on the background object in the predefined orientation may becarried out automatically.

For example, the identifying may be carried out using the Flood-Fillalgorithm, or using OCR (Optical Character Recognition)—say foridentifying a mark such as an arrow that appears on the backgroundobject and points up or the word ‘Left’ when printed on the backgroundobject.

Thus, optionally, the center of the background object, the orientationof the background object, or both, may be identified automatically, asdescribed in further detail hereinabove.

Additionally or alternatively, the center of the background object, theorientation of the background object, or both, may be identifiedmanually by the user, say through a GUI (Graphical User Interface) thatallows the user to manually mark the center or orientation, as describedin further detail hereinbelow.

Thus, in one example, real time video images of the background object ascaptured using the image capture device (say a video camera) of theuser's mobile phone or tablet computer, is presented on a touch screenof the tablet computer or phone.

In the example, simultaneously to presentation of the video images, theuser is allowed to mark the background object's center by touching thescreen, mark an orientation of the object using a multi-touch gesture inwhich the user moves two fingers in a predefined way while touching thescreen, etc., as known in the art of multi-touch gesture recognition.

Subsequently to the marking by the user or the automatic identifying ofthe center, alignment, or both the center and the alignment, thecaptured 210 images may be analyzed 220, and one or more properties ofthe lens may be identified 230 (say by the property identifier 130), asdescribed in further detail hereinabove.

Optionally, the method further includes locating a facial feature (say apupil, nose, etc.) in the captured 210 one or more images and using thelocated facial feature in further steps of the analysis 220.

In one example, the background object is a face of human being and theanalyzing 220 includes using a size of the pupil, nose or other facialfeature located in the captured 210 image, for estimating the locationof a point of discontinuity on the lens. For example, the location ofthe point may be estimated by multiplying the distance of the point froma screw that connects the lens to the eyeglasses' bridge in the captured210 image by a ratio. The ratio is calculated by dividing a known realworld size of the facial feature (say a typical size of an adult'spupil) by the size of the face feature in the captured 210 image.

Optionally, the located facial feature may be used for guiding the userin aligning the image capture device and the eyeglasses in a preferredposition—say in a specific orientation with respect to each other, asdescribed in further detail hereinbelow.

Thus, in one example, the user is guided to move his mobile phone overthe lens. with the phone's camera facing the eyeglasses, to the right,to the left, etc. The user is guided that way, until the face iscaptured 210 partially through the lens and partially directly, with theface's nose captured 210 within a predefined distance from the estimatedcenter of the lens, as described in further detail hereinbelow.

The user may be guided for example, using vocal instructions given onthe mobile phone's speaker, using a GUI implemented using titles withinstructions, arrows, etc. or other cues that appear on the video imagescaptured and presented on the phone's screen as the user moves themobile phone over the eyeglasses, etc.

Optionally, the method further includes applying OCR (Optical CharacterRecognition) in areas along the located boundary, in order to identifylens index data that some manufacturers occasionally mark lens or frameswith, say using a laser beam, as known in the art.

Optionally, the method further includes estimating location of the lenscenter in the captured 210 image.

For example, the estimating may be based on a mass center of the lens ascalculated based on the location of the one or more boundaries, onintersection of a virtual vertical line positioned where the height ofthe lens is maximal with a virtual horizontal line positioned where thewidth of the lens is maximal, etc., or any combination thereof. Thehorizontality and verticality of the lines may be selected, for example,so as to be parallel to the horizontal side and vertical side of asmallest rectangle that would bound the located boundaries of the lens,as known in the art.

Optionally, the estimated location of the lens center may be used inanalyzing 220 the captured 210 image, say for limiting the analysis 220to a predefined central area of the lens.

Optionally, the estimated location of the lens center is used forguiding the user in aligning the image capture device and eyeglasseswith respect to each other, in a predefined orientation in which aspecific part of the background object is captured 210 partially throughthe lens and partially directly (i.e. not through the lens).

Optionally, the method further includes automatically locating a centerof the background object, and using the located center in further stepsof the analysis 220, as described in further detail hereinbelow.

Optionally, the located background object's center is used for guidingthe user in aligning the image capture device and eyeglasses withrespect to each other, say in a predefined, preferred orientation inwhich the background object's center is capture 210 through a preferablearea of the lens, as described in further detail hereinbelow.

Thus, in one example, a GUI implemented using live video images captured210 by the user's mobile phone video camera as the user moves the mobilephone over the eyeglasses, presents the captured 210 video images to theuser in real time, as the user moves the mobile phone over theeyeglasses.

In the example, when the located center of the background object and theestimated location of the lens center—as captured 210 in the videoimages, are close enough, say within a predefined distance from eachother (say a distance of less than 1% of the width of each one of thevideo images), the lens edges are colored green. However, when thelocated center of the background object and the estimated location ofthe lens—as captured in the video images, are not close enough, the lensedges are colored red.

Further in the example, when the edges are colored green, the last imagecaptured 210 by the phone's video camera is forwarded for analysis 220,thus initiating the analysis step 220. However, when the edges arecolored red, an arrow presented on the phone's screen, guides the userto move the phone or eyeglasses in a direction necessary for aligningthe phone's video camera and the eyeglasses in a preferred orientationwith respect to each other.

Optionally, the exemplary method further includes using face detectionmethods applied on the video images—say for identifying that a humanface serving as the background object is captured 210 at least partiallythrough the lens, and for identifying facial features thereof, asdescribed in further detail hereinabove.

The face and facial features my be located in the captured usingsegmentation methods and/or tracking methods, which methods may also beused for automatically locating the eyeglasses, lens, etc.

Thus in a first example, the Viola-Jones cascade object detectionalgorithm and/or one or more other face detection methods, are appliedon the video images captured 210 by the mobile phone or other computer,for locating the face and for identifying one or more facial features,as known in the art.

In a second example, since human eyes are located on the upper half ofthe face, the image's region of interest (ROI) in which the eyes appear,may be located using the Viola-Jones cascade object detection algorithm,an eye corner detection method, a glint detection method, a pupildetection method, etc., as known in the art.

Optionally, in order to reduce computational complexity, the exemplarymethod further uses known in the art video tracking methods.

For example, the method may include updating the ROI in which the eyesappear according to head and eye movements as captured 210 in the image.

Optionally, one or more of the above mentioned Viola Jones, facedetection, segmentation, and tracking methods are further used to guidethe user with the GUI, as described in further detail hereinabove.

For example, the methods may be used for locating the lens boundaries,lens and background object centers, face features, etc., and based onthat locating, to guide the user to move and align the image capturedevice and eyeglasses into a predefined orientation with respect to eachother, as described in further detail hereinabove.

Optionally, in the method, there is further identified 230 an assemblyquality of the eyeglasses based on the analysis 220 (say the analysis220 by the image analyzer 120), as descried in further detailhereinabove.

For example, when the eyeglasses quality of assembly is low, pressureapplied on the lens by the eyeglass frame may have an effect on lighttransmittance in lens areas affected by the pressure. The areas affectedby the pressure are thus expected to differ in their light transmittancewhen compared to light transmittance through parts of the lens that arenot affected by the pressure.

In one example, as a part of the analysis 220 there is identified thatcertain lens areas along the boundaries of the lens as identified (sayby the orientation determiner) appear to have mean RGB channel intensityvalues that deviate significantly (say in more than 10%) from RGBchannel intensity values of the lens' other areas.

Since lenses are typically engaged by the eyeglass frame along parts ofthe lens boundaries, there may thus be identified 230 an assemblyquality of the eyeglasses based on that analysis 220.

Thus, in one example, when the deviation of the RGB channel intensityvalues is lower than a reference value (say a threshold of 10%) aspredefined by a user or programmer of apparatus 1000, there isdetermined that the quality of assembly is high.

However, in the example, when the deviation of the RGB channel intensityvalues in certain areas along the identified boundaries is higher thanthe reference value (say the threshold), there is determined that thereis a problem in the quality of assembly.

Optionally, the property identifier 130 further shows the parts of theframe that may have assembly problems to the user, using a GUI, say bymarking the parts of the lens boundaries along which the deviation ofthe RGB channel intensity values is higher than the reference value, inred color.

Reference is now made to FIG. 2B, which is a flowchart illustrating asecond exemplary computer implemented method for testing of eyeglassesusing a background object, according to an exemplary embodiment of thepresent invention.

An exemplary method for testing of eyeglasses using a background objecteyeglasses, according to an exemplary embodiment of the presentinvention, is implemented on a computer.

The computer may actually include one or more computers, such as a smartphone, a table computer or another computer—in use by a user, a servercomputer in communication with the computer in use by the user, anotherexample, etc. or any combination thereof.

The exemplary method may be implemented by programming the computer, sayby uploading computer executable instructions from a computer readablemedium, etc., as known in the art.

In a first example, the whole steps of analyzing 220 and identifying230, as described in further detail hereinabove, are carried out on afirst computer (say the server computer).

In a second example, the first computer rather communicates with asecond computer, say a remote computer (say a mobile phone or anothercomputer), for carrying out one or more parts of the method steps. Thus,in the second example, at least a part of the below described steps ofanalyzing 220 and identifying 230, is carried out on the secondcomputer.

Thus, the second exemplary method includes the steps of analyzing 220one or more captured images and identifying 230 at least one property ofa lens, using one or more of the one or more captured images, asdescribed in further detail for the first exemplary method hereinabove.

Optionally, the second exemplary method further includes a step (notshown) of receiving one or more images in which a background object iscaptured at least partially through the lens, say from a computer suchas a mobile phone or a tablet computer, as described in further detailthe first method, hereinabove.

In one example, at least a part of the background object is captured inthe image through a lens of a pair of eyeglasses.

Optionally, one or more remaining parts (if any) of the backgroundobject are also captured 210 in the image, but rather directly—i.e. notthrough the lens, as described in further detail hereinbelow, and asillustrated, for example, in FIG. 9.

The one or more images may be received, for example, over the internet,an intranet network, a LAN (Local Area Network), a wireless network,another communication network or channel, or any combination thereof, asknown in the art.

Optionally, the second method further includes one or more additionalsteps as described in further detail for the first method hereinabove.

For example, the second method may include the capturing of the one ormore images, identifying of one or more facial features, guiding theuser, identifying orientation, etc., or any combination thereof, each ofwhich steps may be implemented on the first computer, second computer,or both.

Optionally, in the second method, the step of capturing is carried outon the first computer, say by remotely controlling an image capturedevice (say a mobile 5 phone camera) in use by the user, for capturingthe one or more images, as described in further detail hereinabove.

Reference is now made to FIG. 10A, which is a block diagramschematically illustrating a first exemplary computer readable mediumstoring computer executable instructions for performing steps of testingof eyeglasses using a background object, according to an exemplaryembodiment of the present invention.

According to an exemplary embodiment of the present invention, there isprovided a non-transitory computer readable medium 10000 which storescomputer executable instruction for performing steps of testing ofeyeglasses using a background object.

The computer readable medium 10000 may include, but is not limited to: aRAM (Rapid Access Memory), a DRAM (Dynamic RAM), a ROM (Read OnlyMemory), a PROM (Programmable ROM), a Solid State Drive (SSD), aUSB-Memory, a Hard Disk Drive (HDD), etc., as known in the art.

The computer readable medium 10000 stores computer executableinstructions, for performing steps of the first exemplary method fortesting of eyeglasses using a background object, as described in furtherdetail hereinabove and as illustrated using FIG. 2A.

For example, the medium 10000 stores computer executable instructions1010 for performing the capturing 210 step of the method, computerexecutable instructions 1020 for performing the analyzing 220 step ofthe method, and computer executable instructions 1030 for performing theproperty identifying 230 step of the method.

The instructions may be executed upon one or more computer processors ofa computer in use by the user for testing the user's eyeglasses, on aremote computer in communication with the computer in use by the user(say a remote server computer), on another computer, etc., or anycombination thereof, as described in further detail hereinabove.

The computer used by the user may be for example, a smart phone (say anApple® iPhone or a Samsung® Galaxy cellular phone), a tablet computer(say an Apple® iPad), etc.

In one example, the instructions are in a form of a computer applicationsuch an iPhone® App, which may be downloaded to a mobile phone (say anApple® iPhone), stored on the computer readable medium 10000 (say on thephone's ROM), and executed on the mobile phone's processor.

Reference is now made to FIG. 10B which is a block diagram schematicallyillustrating a second exemplary computer readable medium storingcomputer executable instructions for performing steps of testing ofeyeglasses using a background object, according to an exemplaryembodiment of the present invention.

According to an exemplary embodiment of the present invention, there isprovided a non-transitory computer readable medium 12000 which storescomputer executable instructions for performing steps of testing ofeyeglasses using a background object,

The computer readable medium 12000 may include, but is not limited to: aRAM (Rapid Access Memory), a DRAM (Dynamic RAM), a ROM (Read OnlyMemory), a PROM (Programmable ROM), an EPROM (Erasable ROM), a Micro SD(Secure Digital) Card, a CD-ROM, a Solid State Drive (SSD), aUSB-Memory, a Hard Disk Drive (HDD), etc., as known in the art.

For example, the medium 12000 stores computer executable instructions1020 for performing the analyzing 220 step of the method, and computerexecutable instructions 1030 for performing the property identifying 230step of the method.

The computer readable medium 12000 stores computer executableinstructions, for performing steps of the second exemplary method fortesting of eyeglasses using a background object, as described in furtherdetail hereinabove and illustrated using FIG. 2B.

The instructions may be executed on one or more computer processors ofone or more first computers—say on a server computer, on a remotecomputer in communication with the first computer (say the servercomputer), on another computer, or any combination thereof, as describedin further detail hereinabove.

For example, the instructions may be in a form of a computer applicationwhich may be downloaded to the computer, say to a smart phone (sayApple® iPhone), stored on the computer readable medium 12000, andexecuted on the one or more processors

It is expected that during the life of this patent many relevant devicesand systems will be developed and the scope of the terms herein,particularly of the terms “Lens”, “Eyeglasses”, “Spectrometer”, “Laser”,“Computer”, “Tablet Computer”, “Mobile Phone”, “Smart Phone”, “Screen”,“Camera”, “LCD”, “CCD”, and “CMOS”, is intended to include all such newtechnologies a priori.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled the art. Accordingly,it is intended to embrace all such alternatives, modifications andvariations that fall within the spirit and broad scope of the appendedclaims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention.

What is claimed is:
 1. A method for determining one or more propertiesof an eyeglasses lens, the method comprising: obtaining an image of abackground object, wherein in a first part of the image a first part ofthe background object is captured as viewed through the eyeglasses lensand in a second part of the same image, a second part of the backgroundobject is captured as viewed directly; and analyzing said image bycomparing the first and second parts of the background object beingcaptured, respectively, as viewed through the eyeglasses lens and asviewed directly; and identifying the one or more properties of theeyeglasses lens based on a difference between appearance, in said image,of the first and second parts of the background object viewed,respectively, through the eyeglasses lens and directly.
 2. The method ofclaim 1, wherein the analyzing comprises analyzing a colorcharacteristic of the image by comparing a color characteristic betweensaid first and second parts of the background object captured in theimage, as viewed, respectively, through the eyeglasses lens anddirectly, and thereby identifying said one or more properties associatedwith at least one of a predefined coating and a predefined filter. 3.The method of claim 1, wherein the one or more properties pertain to atleast one of spectral filtering properties and a driving compatibilityof the lens.
 4. The method of claim 1, wherein the one or moreproperties pertain to opacity of the lens.
 5. The method of claim 1,further comprising identifying an assembly quality of the eyeglassesbased on the analysis.
 6. The method of claim 1, wherein the analyzingfurther comprises analyzing image sharpness by carrying out at least oneof the following: processing image of at least the first part of thebackground object viewed through the eyeglasses lens to determinesharpness level of said image of at least the first part in relation toa predetermined threshold; and processing image of the first and secondparts of the background object captured as viewed, respectively, throughthe eyeglasses lens and directly, to determine a difference betweensharpness levels of the image of the first and second parts of thebackground object in relation to a predetermined threshold.
 7. Themethod of claim 1, further comprising identifying a predefineddeformation along a segment within the background object as captured inthe image.
 8. The method of a claim 1, further comprising optimizingcolor selection for at least a part of the background object accordingto technical characteristics of an image capture device intended to beused for capturing the image, a device intended to be used forpresenting the background object, or of both of the devices.
 9. Themethod of claim 1, wherein the background object comprises a pluralityof parts, each part having a respective, predefined color and arespective, predefined position within the background object, and theanalyzing is based on the respective predefined color and position of atleast one of the parts.
 10. The method of claim 1, wherein thebackground object comprises a plurality of parts arranged around acenter of the background object, each part having a respective,predefined color and a respective, predefined order of placement aroundthe center, and the analyzing is based on the respective predefinedorder of placement and color of at least one of the parts.
 11. Themethod of claim 1, further comprising automatically identifying anorientation of the background object as captured in the image.
 12. Themethod of claim 11, wherein said automatically identifying of theorientation of the background object captured in the image comprisesusing a directional aspect of a texture of the background object ascaptured in the image.
 13. The method of claim 11, further comprisingautomatically initiating the analyzing of said at least part of theobject, upon identifying alignment of the background object as capturedin the image in a predefined orientation.
 14. The method of claim 1,further comprising guiding a user in aligning the pair of eyeglasses andan image capture device used to capture the image with respect to eachother.
 15. The method of claim 14, further comprising at least one ofthe following: locating a facial feature in the captured image, andusing the located facial feature for said guiding of the user;identifying alignment of the background object as captured in the image,and using the identified alignment for said guiding of the user locatinga boundary of the lens in the image, and using the located boundary forsaid guiding of the user; automatically estimating a location of acenter of the lens of the eyeglasses in the image, and using theestimated location for said guiding of the user.
 16. The method of claim1, further comprising locating a boundary of the lens in the image. 17.The method of claim 16, further comprising verifying that the backgroundobject as captured in the image extends over two sides of the boundary.18. The method of claim 1, further comprising automatically estimating alocation of a center of the lens of the eyeglasses in the image.
 19. Asystem for testing of eyeglasses using a background object, the systemcomprising: a reference object image provider configured and operablefor obtaining an image of a background object, wherein in said image afirst part of the background object is captured as viewed through a lensof the pair of eyeglasses and in the same image a second part of thebackground object is captured as viewed directly; an image analyzerconfigured to analyze the image of the background object to determinesaid part of the image in which said first part of the background objectis captured as viewed through the lens of the eyeglasses and said partof the same image in which the second part of the background object iscaptured as viewed directly; and a property identifier, in communicationwith the image analyzer, configured to compare the first and secondparts of the background object as captured in said image and identifythe one or more properties of the eyeglasses lens based on a differencebetween appearance, in said image, of the first and second parts of thebackground object viewed, respectively, through the lens and directly.20. A non-transitory computer readable medium storing computerexecutable instructions for performing steps of testing of eyeglassesusing a background object, the steps comprising: obtaining an image of abackground object, wherein in a first part of the image a first part ofthe background object is captured as viewed through a lens of a pair ofeyeglasses and, in a second part of the same image, a second part of thebackground object is captured as viewed directly; and analyzing saidimage by comparing the first and second parts of the background objectbeing captured, respectively, as viewed through the eyeglasses lens andas viewed directly; and identifying the one or more properties of theeyeglasses lens based on a difference between appearance, in said atleast part of the image, of the first and second parts of the backgroundobject viewed, respectively, through the lens and directly.